Irisssss

ex big-tech recruiter | Referrals, recruiter strategy, real interview prep | Building @ScrenaAI_ to make it easier.

[Irisssss @ScrenaAI]

AI amplifies expertise. It can't generate it. and companies are deleting the expertise before they realize they needed it.

[Andrew Lokenauth @FluentInFinance]

Ford just gave us one of the clearest lessons of the AI era.

They tried to replace humans with AI but had to bring humans back.

Ford fired 350 engineers. The plan looked smart on paper. Cut salaries. Let AI handle the work. Boost margins. It didn't work.

AI failed to meet its quality checks.

Ford underestimated the value of decades of human judgment.

AI can't replace someone who knows when something "looks fine" but feels wrong.

AI is a tool. Experience is still the moat.

[Irisssss @ScrenaAI]

"52 small layoffs a year" is the most accurate description of 2026 I've seen.

The worst part isn't getting cut. It's everyone staying just anxious enough to never feel safe and never leave.

[Blind @JoinBlind]

Layoffs went from a one-time event to a subscription model. 💀

[Irisssss @ScrenaAI]

You're not being rejected by recruiters.

You're being rejected by the same model, over and over, that decided no before anyone read a word.

[Sakshi Sugandhi @SakshiSugandhi]

Tech recruiters be like 😈

"Ask them to upload their resume.
Now make them fill the same info in 20 fields again. Now send the instant noreply rejection in under two minutes."

[Irisssss @ScrenaAI]

They sold an entire generation "just learn to code" and then deleted the first rung the year they showed up.

I recruited at big tech and watched it happen. juniors didn't get replaced. they just stopped getting hired.

[Shanaka Anslem Perera ⚡ @shanaka86]

JUST IN: AI is not coming for your job. It is coming for the first one, the entry-level rung that turns a graduate into a professional, and that is the far more dangerous outcome. Recent college grad unemployment has climbed to 9.7 percent, the same odds of being jobless as people who never went to college at all.

For almost three years everyone braced for the same nightmare, a sudden bloodbath where AI fires millions of experienced workers overnight. The data shows the opposite. Overall unemployment is fine, mid-career professionals still have their desks, and the damage is landing in one quiet place: the bottom of the ladder.

The numbers are stark. Look for yourself! Computer science graduates, sold for a decade as the safest bet in the economy, now face 7 to 8 percent unemployment, as high as fine arts majors. Two separate studies put the rate at which young people enter knowledge work down about 14 percent, and Stanford measured young software developers down nearly 20 percent from 2024. Goldman estimates AI is already erasing 16,000 jobs a month, and one widely cited tracker has logged 55,000 cuts blamed directly on it. The mechanism is brutally plain. AI now does the structured grunt work companies used to hire juniors to do, so they have stopped hiring juniors.

If you look closely, here is the part almost no one is saying, and it outweighs any single number. Those entry-level jobs were never just labor. They were the training ground. You become a senior engineer by first being a junior one. You become a partner by first spending years as an associate buried in the boring work. The grunt work was always how judgment got built, quietly, on the job. If AI eats the bottom rung, the ladder does not simply get shorter. It loses its first step, and a profession that can no longer bring anyone up from the bottom slowly stops producing the experts at the top.

That is indeed the trap hiding inside a calm jobs report. We are not just automating tasks. We are automating the apprenticeship that turns a novice into a master. And the bill does not arrive now. It arrives in ten or fifteen years, when today's senior people retire and there is a thin, strange gap where their replacements were supposed to be. You cannot have masters without first having apprentices, and AI just made the apprentice look redundant.

The honest counter is very real. The aggregate has not cracked, unemployment for degree holders is still historically low, more than three quarters of last year's graduates found work within three months, and internship postings have risen 32 percent. Plenty of serious economists argue this is a productivity boom that will create more roles than it destroys, the way every major technology eventually did. The entry-level decline is a signal to watch, not yet a verdict, and the loudest warning of all, a major AI chief predicting a white-collar bloodbath, has not shown up in the headline numbers.

But the shape is hard to ignore. The real danger of Ai was never a dramatic day when everyone gets fired. It is this. A quiet year when no one gets hired at the bottom, every ladder loses its first rung at once, and the loss only becomes visible a decade later, when you go looking for someone who climbed it and find no one there.

[Irisssss @ScrenaAI]

Is your interview prep mostly just grinding LeetCode?

For technical and research roles at OpenAI, Anthropic, and xAI, that's not what matters most.

Here's what they actually look for:

[Irisssss @ScrenaAI]

@CTL766 这里这里诚信互关

[Irisssss @ScrenaAI]

@DJl_ins888 这里这里 诚信互关

[Irisssss @ScrenaAI]

@Mr57814170 @inaswijaya yes！

[Irisssss @ScrenaAI]

One thing I'd add from the recruiting side: getting the interview is its own hard problem right now. I recruited at big tech, and the truth is most strong candidates never even reach a person, the application pile is mostly an algorithm now.

So keep all of this, but carve out some of that daily time for referrals and reaching out to people directly. The prep pays off way more once the door's actually open.

[divyansh @Divyansh91565]

This is how I planned to spend 2-3 hours daily preparing for interviews:

- I'm already comfortable with DSA, but I'll finish NeetCode 250 as a structured revision.
- I'll start recording myself explaining solutions and thinking out loud while coding.
- Adding 2 hours dedicated to CS fundamentals.

Q1. How do I improve communication while coding and explaining my thoughts?

- Record myself solving problems and explain every decision as if I'm talking to an interviewer.
- After solving, spend 2-3 minutes summarizing the approach, complexity, and stuff.
- Watch strong interview walkthroughs and observe how candidates structure their thinking.

Q2. Best resources for CS fundamentals (interview-focused)?

- OOP, OS, DBMS, and CN notes from TUF.
- GeeksforGeeks CS Core Subjects for revision.
- DBMS by Kunal Kushwaha.
- Operating Systems by Gate Smashers.
- Interview-specific question lists from InterviewBit and GFG.

Q3. Behavioral round preparation?

- Learn the STAR format (Situation, Task, Action, Result).
- Prepare stories around:
- Biggest challenge
- Failure and lessons learned
- Leadership experience
- Team conflicts
- Tight deadlines
- Strengths and weaknesses
- Why this company?

Since I'm still in college and not getting interview calls yet, I plan to use hackathons, projects, contests, and team assignments for these stories.

The goal isn't to crack interviews tomorrow. It's to avoid starting from scratch when an opportunity finally comes.

If you think there's a better way to prepare, I'd genuinely appreciate your suggestions.

[Irisssss @ScrenaAI]

If you're a new grad sending 500 applications into the void, here's the chart that explains why.

Tech's share of total employment climbed for almost 20 years straight. Then it peaked, right around when ChatGPT dropped, and it's been falling below trend ever since.

You're not bad at job hunting. You're job hunting in the first real contraction this industry has had in your lifetime. The old playbook (apply, wait, repeat) was built for the line going up. It's not going up.

*Sauce: BLS, Haver Analytics, Goldman Sachs Global Investment Research (GIR)

[Irisssss @ScrenaAI]

Not on you. There's a new Stanford study showing most applications get screened out by an algorithm before a human ever sees them, 920 apps, mostly filtered by a machine.

I recruited at big tech. The few who get through almost always come in through a referral, not the pile. That's the move, not more applications.

[KlawWrld @kawhi_ring]

920 job applications for IT/cyber summer internships
6 interviews
1 job offer (shit, declined)

I graduate in December I’m gonna be so cooked I might have to join the Air Force

[Irisssss @ScrenaAI]

As someone who recruited for big tech: people think "applied and got rejected." The truth is usually worse, you were never evaluated by a person at all.

The ones who got through? They had someone inside drop their name before the algorithm ever ran. Referrals don't just "help." They put you in a completely different lane.

[Nav Toor @heynavtoor]

Stanford researchers proved you are not being rejected by 10 companies. You are being rejected by one algorithm 10 times.

Your score is stored for 330 days. Every company that uses the same vendor sees the same number. They call it the algorithmic blackball.

Researchers at Stanford HAI, Chapman University, and Northeastern University published the largest audit of AI hiring algorithms ever conducted.

The paper is called "Algorithmic Monocultures in Hiring." Published at FAccT 2026, May 26. The data came from Pymetrics, the AI hiring platform used by major Fortune 100 companies.

Here is what they found.

When you apply for a job at a company that uses Pymetrics, you play a series of assessment games. Your scores are stored. For up to 330 days. If another company also uses Pymetrics, your application is evaluated using the same stored scores. You are not getting two separate evaluations. You are getting the same score twice.

If the algorithm rejects you once, it rejects you everywhere.

The researchers call this the "algorithmic blackball." One bad score locks you out of every company that shares the same vendor. You never find out why. You never get a second chance. You just stop hearing back.

They ran a large-scale simulation using real applicant data. The result: over 40,000 job advances were lost because applicants who would have succeeded at one company were screened out by an algorithm calibrated for a different one.

Then they measured who gets hit hardest.

25.87% of Black applicants were routed into algorithmically discriminatory hiring processes. 14.74% of Asian applicants. These are not hypothetical projections. These are rates measured in deployed, real-world hiring systems used by some of the largest employers on earth.

The same algorithm. Applied across companies. Producing the same racial disparities at every one of them.

This is already in the courts. Mobley v. Workday is a federal class-action lawsuit alleging that AI hiring tools systematically discriminate against older, Black, and disabled applicants. The case is ongoing.

In Europe, the EU AI Act classifies hiring algorithms as high-risk AI systems by default. Compliance requirements take effect August 2, 2026. Weeks away.

In the United States, there is no equivalent federal law.

The researchers make four recommendations. Measure adverse impact at the position level. Strengthen cross-employer surveillance. Monitor risks from algorithmic concentration. Create legal pathways for independent researchers to access hiring data.

The last one carries an implicit warning. This study was only possible because Pymetrics voluntarily shared its data. Most vendors would prefer their algorithms remain opaque.

The next time you apply for a job and never hear back, the rejection may not have come from a human. It may have come from a score you received 330 days ago, at a company you have already forgotten, for a role that had nothing to do with the one you just applied to.

[Irisssss @ScrenaAI]

Impact is most visible for entry-level candidates:

Fewer roles + more experienced competition = higher interview bars.

As a result, understanding the industry, information asymmetry, and how interviews actually work has become more important than ever.

Because in this market, it's no longer just about being qualified — it's about knowing how the system filters candidates.

[St. Louis Fed @stlouisfed]

A new analysis looks at job openings, hiring and layoffs to quantify the “low-hire, low-fire” labor market nationally and in the Fed’s Eighth District https://t.co/7Es04Cry2n

[Irisssss @ScrenaAI]

The tech career path today is a completely different game.

You're facing brutal competition and AI pressure.

Even interviews have become exponentially harder — it feels like a whole new game with much tougher rules.

[ayesha @ayesha_fatiima]

I am feeling jealous whenever I see 35+ (age) guys in tech.

- Entered IT when there was no competition.
- Now in senior, director, or managerial positions
- Probably earning crores.
- Built their careers before AI became a threat
- Rarely had to worry about layoffs during their peak years
- -They are the main reason many parents forced their children to become software engineers.
- Have accumulated enough wealth that job security is no longer a major concern

[Irisssss @ScrenaAI]

@RoseOnX9 started CS in 2019 thinking i'd graduate into generational wealth 😭
graduated to find out a live-in nanny out-earns me. at least AI can't replace the nanny yet.

[Irisssss @ScrenaAI]

@all7i8n9 来了

[Irisssss @ScrenaAI]

interviewer: how do you approach problem solving？
#ai

[Irisssss @ScrenaAI]

@araseb_ lol i use codex to do research

[Irisssss @ScrenaAI]

The thing everyone's missing: that's not 155k people out of work. It's 155k people with real big-tech experience all hitting the market at once.

So when you get ignored, it's not you. you're up against laid-off seniors for the same job now. Apply-and-wait is just done this year — pile's too deep, too good.

The only thing still working is referrals and going straight to recruiters instead of the application black hole. Kind of the whole reason i'm building what i'm building.

[Polymarket Money @PolymarketMoney]

JUST IN: Tech companies have now laid off more than 154,995 people so far this year.