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Artificial IntelligenceData Faces Podcast

The perfect storm behind the tech job market

David Sweenor

Executive recruiter Jim Jinright on judgment capital and moving up the stack

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▶ Watch the full episode and read the transcript: Jim Jinright on the AI job market and judgment capital

The Data Faces Podcast with Jim Jinright, Managing Partner, JRJ Search Group
The Data Faces Podcast with Jim Jinright, Managing Partner of JRJ Search Group

When I started at IBM, the site had about 13,000 people. By the time I left, it was closer to 3,000, and somewhere in between I absorbed the lesson that no company was going to keep me around any longer than tomorrow. A few RIFs, acquisitions, divestitures, etc. proved to me that this is how the business world behaves — it's cruel, unapologetic, and without remorse. So when the early-career people I mentor tell me that AI took the jobs they were promised, I understand the feeling, and I also suspect the story is more complicated than the headline.

I wanted a second opinion from someone who reads the market for a living, so I asked Jim Jinright to join the Data Faces Podcast. Jim runs an executive search firm, and he spends his days talking to the CEOs, CIOs, and CTOs who decide whether a requisition opens or stays closed. His answer to "is it AI?" is mostly no. He sees a perfect storm that arrived a couple of years before the AI headlines did, and AI just happened to be standing there when the blame got assigned.

About Jim Jinright

Jim Jinright is the managing partner of JRJ Search Group, an executive search firm serving the Dallas-Fort Worth technology market. He has been in search for about twelve years and took over JRJ, the firm his father founded, after running CIO, CTO, and chief data officer searches for a regional boutique. JRJ places strategic and technical people in mid-market companies.

In this episode, Jim and I discuss:

  • Why the layoffs blamed on AI trace back to pandemic overhiring, the 2022 interest rate increases, and labor arbitrage
  • Where the junior work went, and why senior people with judgment are more in demand than before
  • What a recruiter does when 500 applications arrive within an hour, and how a candidate gets from 500 to the final five
  • Ghost jobs, age screens, and AI interviews, and what a candidate can do about each
  • Jim's advice for early-career engineers: move up the stack from writing code to running the software factory

Watch the full conversation here:

YouTube player

A perfect storm with AI's name on it

Jim does not rule out layoffs caused by AI, and he thinks AI could create more jobs than it removes once the dust settles. I vaguely recall something about Schumpeterian destruction, but I digress. I made a version of that argument in January 2024 in Will AI take my job? Maybe.: every technology wave so far has created more jobs than it destroyed, just not for the same people. Jim simply doesn't believe AI is what's been happening in the searches he runs. His account starts with the pandemic, when companies hired far more people than they needed. Around 2022, interest rates rose, capital got expensive, and the startups and mid-market companies that had been expanding pulled back. Cost cutting followed, and much of the work that stayed went offshore.

AI arrived on top of all that, bringing a seductive idea with it. Maybe you do not need 50 engineers. Maybe you need 10, or none. Doug Laney told me on this show that he expects the first billion-dollar business run by a handful of people and a swarm of AI agents. Jim thinks the notion is correcting as companies realize they still need people to make decisions and build things, and he is getting more calls for talent than a year ago.

He calls the period we are leaving the no-hire, no-fire economy and the one we are entering the low-hire, no-fire economy. That is a change in direction rather than a boom, but for anyone who has been sending applications into a void, it matters that the void is starting to answer.

"I wouldn't attribute all the layoffs to AI per se. I think a lot of people call this the no-hire, no-fire economy, and now we're moving into the low-hire, no-fire economy."

— Jim Jinright, Managing Partner, JRJ Search Group

Where the junior work went

Jim's explanation for who has been affected starts with a simple observation: AI is powerful, but it has no context. Senior people are more in demand than before, since companies want someone who can review everything the model produces and say which facts matter and whether the work is right. That person knows what good looks like, and Jim says that is not an easy find.

Junior people have been hit harder, and the mechanism is specific. Jim would never hand a new recruiter his biggest client on day one, but over time he would train that person up to it, and the small problems were the training. Now much of that work goes to AI, and the ladder loses its bottom rungs.

On the engineering side, it takes real engineers to build AI systems, and anyone who has not built agents or put one into production is, in Jim's words, probably talking a big game. Knowing how to use ChatGPT is not the bar. The requirement is someone who can own the outcome and produce a return, and Jim called that, when we spoke before the recording, judgment capital. Human capital used to sit above financial capital in what a company needs most. With AI doing the work at the bottom, everyone moves up the stack to judgment.

Every resume is perfect now

A client of Jim's posted a position and received more than 500 applications within an hour. The company couldn't get through them, and that is the state of hiring right now. Candidates use AI to apply, often to the same job more than once, and AI sorts the applications. In some companies, AI runs the first interview. Every resume has the right words, and every LinkedIn profile is polished, so the signals a recruiter used to rely on have stopped meaning anything.

Jim gets through the noise with conversations, a lot of them. AI can surface 500 profiles, but getting from 500 to five, and then to the one person a client should hire, means talking to 30, 40, or 50 people. He describes a hiring manager as a human being crying out for help, and the way through is to show that person you can solve their specific problem.

"That's how you differentiate yourself, is just being able to bring the receipts with what you've done. Problem solved, value created."

— Jim Jinright, Managing Partner, JRJ Search Group

Bringing the receipts means problems you owned and outcomes you delivered, with enough detail that the claim can be checked. Emotional intelligence and extreme ownership are the traits Jim's clients ask for, and neither survives being written on a resume. Jim can tell within minutes whether someone is transactional or someone who cares about the problem. He wants to hire the person who would show up to open the store after a snowstorm. I added my own advice: be yourself in that conversation, because landing in the wrong role by pretending is bad news for everyone involved.

Ghost jobs, age screens, and the interview bot

People I mentor keep asking whether the listings they see are real, the same job posted in fifteen cities that never seems to close. Jim has seen them too, and executives he knows have confirmed the practice: some companies advertise roles that do not exist to build a talent pipeline for later. He calls it a horrible thing to put a person through when their back is against the wall. His larger point is one I wish someone had told me during my first layoff. When responses aren't coming, it is not always you. Sometimes it is just the moment.

I also asked him about age, since more than one experienced person has told me about being waved off by a five-year-old company that only hires 20-year-olds. Show the last ten years rather than a snapshot of thirty, and lead with the value. People with experience, judgment, and a track record are in demand right now, and smart companies understand that hiring across the spectrum benefits them. Job hopping got a similar treatment. Most careers now run two years here and three there, and that reads fine as long as the resume explains the moves rather than leaving a hiring manager to guess.

The AI interview drew Jim's sharpest reaction of the conversation. A company that cannot spare 30 minutes for a screening call is not going to land a rock star who already has a job, and putting a professional into a funnel to talk to a bot is, in his words, a slap in the face. The candidate is evaluating you too, and none of his clients hire that way. Leigh Felton made a related point on this show. Had AI been screening candidates when she started out, it would have rejected the person who went on to run Microsoft's first ethical AI enablement program.

Move up the stack

When Jim started recruiting, every college graduate had something like ten open positions to choose from, and today the ratio has inverted. He feels for the engineers who finished a computer science degree and cannot find work. Coding is becoming a commodity because the AI can code, so his advice to a junior person is to stop competing on React or Kotlin and move into the judgment sphere. Be the person who can build the software factory: set the guardrails, decide what should and should not be built, and learn to manage an AI system once it is running. Strong technical skills still matter, and strong communication has to travel with them.

He also has a specific move for anyone whose applications are going nowhere. Stop asking people for a job. Identify leaders in your industry, tell them you researched their background, and ask for advice on which user groups and masterminds to join. Some of those leaders open doors later. Fractional and contract work fits the same pattern. Companies like bringing in an expert for a season while they figure out what to invest in. It brings in revenue, expands your network, and keeps you sane, which he says is the real job during a search.

So here is what I took away. AI is not hiring or firing anyone. Interest rates, overhiring, and cost-cutting did most of the damage, and the correction has started. What AI has changed is where the value sits, and it sits in judgment. Build the receipts that prove yours.

Listen to the full conversation with Jim Jinright on his Data Faces Podcast episode page.

Based on insights from Jim Jinright, Managing Partner at JRJ Search Group, featured on the Data Faces Podcast.

Podcast highlights

  • [0:06] Introduction and welcome to the Data Faces Podcast
  • [1:11] JRJ Search Group, twelve years in search, and the Dallas Fort Worth market
  • [2:16] A would-be pilot, a demo tape, and the uncle who ran A&M Records in Canada
  • [3:33] Is it AI? The perfect storm behind the layoffs
  • [4:10] Pandemic overhiring, 2022 interest rates, labor arbitrage, and the "10 or none" idea
  • [5:45] From no-hire, no-fire to low-hire, no-fire
  • [6:16] AI has no context, and why senior judgment is in demand
  • [7:30] The small problems used to go to the junior person
  • [8:23] Every resume is perfect. How does anyone stand out?
  • [9:04] 500 applicants in an hour, and getting from 500 to five
  • [10:44] LinkedIn as the largest talent database, and why the network still wins
  • [11:19] Ghost jobs, and why it is not always you
  • [13:35] Dave's own layoffs, and IBM going from 13,000 to 3,000
  • [14:38] Staying sane during a search
  • [16:03] Age discrimination, and showing the last ten years
  • [18:24] What "AI literate" means, and the skills clients ask for
  • [21:31] How do you demonstrate emotional intelligence on paper?
  • [23:04] Behind every job posting is a human being asking for help
  • [24:43] Be yourself, and the rise of fractional work
  • [26:31] The AI interview, and why Jim calls it a slap in the face
  • [28:44] Job hopping, acquisitions, and the resume that explains itself
  • [32:57] Advice for early-career engineers: move up the stack
  • [35:20] Ask leaders for advice, not for a job
  • [36:57] Landman, boat adventures on YouTube, and being an information junkie
  • [38:11] Where to find Jim

Frequently asked questions

Is AI causing the tech layoffs?

Mostly no, according to executive recruiter Jim Jinright of JRJ Search Group. The tech job market slowdown traces to a combination of forces that arrived before the AI headlines: overhiring during the pandemic, the interest rate increases that began in 2022, and the cost cutting and labor arbitrage that followed. AI added the idea that a company might need ten engineers rather than fifty, but Jim sees that notion correcting as companies realize they still need people to make decisions and build things.

What is a "low-hire, no-fire" economy?

A low-hire, no-fire economy is a job market in which companies are not laying people off in large numbers but are opening only a small number of new positions. Jim Jinright uses it to describe the market in late 2026, as it moves out of the no-hire, no-fire period that followed the 2022 rate increases. He reports more calls for talent than a year earlier, which he reads as an early sign of a turn.

Which jobs are most affected by AI?

Junior roles are the most affected, because AI now does much of the smaller, well-defined work that junior staff used to learn on. AI is powerful but has no context, so companies are hiring senior people who can judge which of its outputs matter and whether the work is correct. On the engineering side, demand is strongest for people who have built and run AI systems, rather than people who know how to use a chatbot.

How do you stand out when every resume is written by AI?

Through conversations, and by bringing the receipts. When a posting draws 500 applications in an hour, AI can sort the pile, but a recruiter still has to talk to dozens of people to find the one worth hiring. Candidates who differentiate themselves show specific problems they owned and outcomes they produced, in enough detail to be verified. Traits like emotional intelligence and ownership only show up in that conversation, never on the page.

What is judgment capital?

Judgment capital is Jim Jinright's term for the value a person adds by knowing what good looks like and which facts matter, and by taking responsibility for the outcome. Human capital used to sit above financial capital as the scarce resource in a company. With AI absorbing the routine work, the scarce resource moves up the stack to judgment, and companies pay for people who have it.

What should an early-career engineer do about AI?

Move up the stack. Jim's advice is to stop competing on a specific language and become the person who can build the software factory, which means setting the guardrails and deciding what should and should not be built, and then managing the AI system once it runs. Pair that with strong communication, and replace cold job applications with requests to industry leaders for advice on which user groups and masterminds to join.

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 10 AI thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

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Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

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