How AI Is Being Trained to Do Your Job

Summary

Podcast Summary: The Journal – “How AI Is Being Trained to Do Your Job”

Date: June 4, 2026
Hosts: Ryan Knutson, Jessica Mendoza
Reported by: Katie Bindley
Episode Theme:
This episode explores the rapidly growing new gig economy where professionals are hired to train artificial intelligence (AI) models to do specialized, often white-collar, jobs. Focusing on startup Mercor and its global network of contractors, the episode examines the promise, the practicalities, and the perils of this evolving form of work.

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Main Theme Overview

The episode centers on the rise of a novel job category: people leveraging their professional expertise to train AI models to execute the very work they used to do. The hosts and guest reporters profile the startup Mercor—now reportedly worth $10 billion—which acts as an intermediary between AI companies and human contractors. The conversation dives into the mechanics of this emerging gig work, the experiences and mixed feelings of contractors hired by Mercor, and ethical and legal controversies surfacing along the way.

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Key Discussion Points and Insights

1. The Business of Training AI (03:51–07:05)

• Mercor’s Model:
  Mercor connects AI companies (e.g., OpenAI, Anthropic) with human contractors who use their domain-specific knowledge to improve AI's capability in real-world tasks (04:06).
• Who they hire:
  Job postings span diverse fields—from investment banking, legal, accounting, and therapy, to acting and dermatology (00:58–01:25).

  “It’s like hard to think of jobs that they’re not hiring for… screenwriters, all kinds of things.” — Katie Bindley (01:25)

• Demand for Human Data:
  AI models have consumed most public data; now, unique human knowledge and data are essential for further improvement (04:06).

2. Personal Experience of an AI Trainer – Carolina’s Story (04:48–12:11)

• Background:
  Carolina Perez Sands, a trilingual speech and language pathologist, was recruited on LinkedIn for her expertise (04:52–06:05).
• Roles:
  Her job was to help AI understand nuances in European Portuguese, identify overly generic or cliché writing, and flag unnatural language (“AI-isms”) such as everything happening on a Tuesday (08:11–11:01).

  “At some point, everything happened on a Tuesday… So really, because they have learned… you need to write specific things and land everything in real life.” — Carolina Perez Sands (10:33)

• Compensation and Appeal:
  Initial pay was $45/hour (07:28). The appeal included the prestige of being “on the cutting edge of technology” (07:34).

3. Bittersweet Reality for Gig AI Trainers (11:12–12:48)

• Mixed Emotions:
  Some contractors are excited; others feel resigned, seeing this as a last resort as AI threatens to automate their field.

  "It's almost like you're burning down the building behind you… if I can't get a job in this industry, nobody can." — Ryan Knutson (11:57) “There was this sort of sense of inevitability… the writing’s on the wall.” — Katie Bindley (11:18)

• Mercor’s Claim:
  “We are paying experts more than $3 million a day… every major technology shift has looked like a jobs crisis, but the consistent pattern is that it creates new categories of work.” (12:14)

4. Ethical and Legal Issues (13:51–17:31)

• Pressure to Share Prior Work:
  Some contractors are asked (or feel pressured) to provide prior work samples that may be owned by previous employers, potentially breaching IP (14:03–15:12).

  “Guys… sign NDAs that are like the thickness of a book and everything’s under NDA… Mercor offering to purchase this data… they were like, dude, we definitely don’t own this stuff.” — Katie Bindley (14:03)

• Mercor’s Response:
  Claims only to license material contractors own, denies soliciting or buying employer-owned IP (15:14).
• Data Breach and Lawsuits:
  Mercor suffered a significant data breach, exposing contractors’ personal information and work activity. Class action lawsuits allege improper sharing of contractor data—including job interview recordings and computer screenshots—with clients (15:39–16:48).

  “Mercour makes the point that… you are only to be working on your Mercur related projects… if you have two monitors, you should only have your Mercur related work up on either screen at any time.” — Katie Bindley (16:48)

5. The Rapid Pace of Automation and its Consequences (17:31–21:20)

• Explosive Growth:
  Mercor hired 30,000 contractors in the past year and was valued at $10 billion after a massive fundraising round (17:31–17:43).
• The Uncertain Future for Human Trainers:
  Competition is increasing (Handshake AI, Micro One, Surge). As models improve, the demand for trainers can vanish overnight, as Carolina experienced:

  "That's when I worked myself out of a job… in a week's time, you didn't have to correct anything." — Carolina Perez Sands (18:58)

• Contractor Disillusionment:
  As tasks became less rewarding and pay shrank from $45/hr to per-task rates (~$20/task), Carolina felt she was contributing to “a monster” and left the job (19:32–20:49).

  “I started to think my job is actually making this more of a monster… we're not connecting people to people. So, yeah, I started to feel very… disillusioned." — Carolina Perez Sands (19:32)

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Notable Quotes & Memorable Moments

• AI Training as Tutoring:

  “The first phase of training AI is like reading every book in the library… Now, it’s like going to sit down with a tutor.”
  — Ryan Knutson & Katie Bindley (01:49–02:00)

• Job Listings and Scope:

  “It’s like hard to think of jobs that they’re not hiring for.”—Katie Bindley (01:25)

• Catching AI Clichés:

  “At some point, everything happened on a Tuesday.”—Carolina Perez Sands (10:33)

• Contractor Motivation:

  “There was this sort of sense of inevitability… the writing’s on the wall.”—Katie Bindley (11:18)

• Fear of AI’s Impact:

  “I started to think my job is actually making this more of a monster… not for me, but a waste of collective time.”—Carolina Perez Sands (19:32)

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Key Timestamps

• 00:05–01:25 — Introduction to the new AI training gig economy and discussions of wide-ranging job postings.
• 03:51–07:05 — Mercor’s rise, business model, clients, and need for “fresh” human data.
• 07:05–11:12 — Carolina’s experience as a contractor; practical tasks, successes, challenges.
• 11:12–12:48 — Emotional and economic realities for contractors; Mercor’s claims about job creation.
• 13:51–17:31 — Legal, privacy, and ethical challenges: data IP issues, data breach lawsuits.
• 17:43–19:17 — Mercor’s hypergrowth; competition from rival startups.
• 18:58–21:20 — Carolina’s disillusionment and withdrawal; existential questions about the future of AI and knowledge work.

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Tone & Language

The conversation balances journalistic curiosity and skepticism, matched by the candid, sometimes resigned tone of contractors who have found themselves training away their old roles. The overall mood is thoughtful, uneasy, and at times darkly humorous (“like you’re burning down the building behind you”).

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Episode Takeaways

• AI training gigs are rapidly growing and encompass a vast array of professions.
• Contractors find the work intellectually engaging but are often conflicted—caught between opportunity and the realization that they may be training AI to replace themselves.
• The sector is facing significant ethical, legal, and privacy challenges—especially regarding IP ownership and data security.
• While there’s enormous investor enthusiasm, the future scale and permanence of this work remain uncertain, as automation hastens the obsolescence of even the trainers themselves.

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Transcript

[Ryan Knudsen]

There's a new type of gig work that's growing rapidly right now. It involves talking to AI and teaching it how to do your job.

[Katie Bindley]

There is this new sort of category of jobs that basically involve people sitting in front of a computer and helping to train AI to perform some of the things that they are experts in or what they used to do in their careers pretty much.

[Ryan Knudsen]

Our colleague Katie Bindley covers tech and she's been reporting on a startup that's doing this kind of work and it's hiring like crazy. It's called Mercor.

[Katie Bindley]

The best ways to sort of get insights about this type of work is to just look at the job postings that are available. They put out a gazillion postings on LinkedIn.

[Ryan Knudsen]

Like, can we look at some of their LinkedIn posts?

[Carolina Perez Sands]

Oh, let's.

[Katie Bindley]

Okay, so you'll see tons of posts. Start your month with Work from Home. Explore flexible remote opportunities with Mercur and work from anywhere. Apply here. It'll basically say, like, we are looking to hire someone with experience as, you know, an investment banker or entry level analyst or whatever. And it lays out specifically what they're looking for. And I think the idea is to help the AI perform the duties of those roles.

[Ryan Knudsen]

Are they ever looking for podcast hosts?

[Katie Bindley]

I've seen ones for like voice actors and stuff. I mean, the range of job postings, it's like hard to think of jobs that they're not hiring for. I mean, they were looking for dermatologists, radiologists, therapists, like accountants, consultants, actors. I mean, screenwriters, all kinds of things.

[Ryan Knudsen]

Is there a metaphor that you could use for like this phase of AI training? Like the first phase of training AI is like reading every book in the library.

[Katie Bindley]

Yeah.

[Ryan Knudsen]

And so now going to sit down with a tutor maybe?

[Katie Bindley]

Yeah, I think actually a tutor is like a really good way to put it. It's certainly like moving us toward the direction of AI being able to handle more and more complex sort of white collar work related tasks.

[Ryan Knudsen]

Welcome to the Journal, our show about business and power. I'm Ryan knudsen. It's Thursday, June 4th. Coming up on the show, the new gig work that's training AI to take your job.

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[Ryan Knudsen]

Merkor was founded in 2023 by three college dropouts. It's become a $10 billion company in less than three years. It's basically a middleman between large AI companies and humans who can help train large language models.

[Katie Bindley]

AI companies need a lot of data in order to improve their models. And the gist is that, you know, most, if not all of the publicly available data has already been used. So in order for the AI to get smarter, if you will, it needs fresh sources, fresh materials to train on. And humans turn out to be very good sources of that. Merkur has clients. Their clients have included some of the biggest AI companies, including OpenAI and Anthropic. And so they're serving as sort of this go between. And Mercur hires the contractors, and so then the contractors are the ones who are helping to improve the models.

[Ryan Knudsen]

One person who's worked as a contractor for Mercur is Carolina.

[Carolina Perez Sands]

I'm Carolina Perez Sands. I'm originally from Spain, from Madrid. I grew up in three languages, right? So my parents are from Spain, but we lived in Lisbon. So my friends were Portuguese and then I went to a French school. So I grew up in three languages simultaneously and I was always very interested in words.

[Ryan Knudsen]

Carolina came to the US over a decade ago, went to graduate school and spent a big part of her career working as a speech and language pathologist.

[Carolina Perez Sands]

So what I did was I helped people for whom voice was their main job instrument, if you want. So I helped journalists, reporters, TV and radio presenters, actors, singers, lawyers, everybody who needed to have a voice that sounded powerful.

[Ryan Knudsen]

Recently, while trying to grow her business, she started promoting her language and writing skills on LinkedIn. And it led her to a job she wasn't expecting, training AI.

[Carolina Perez Sands]

This was March 2025. I was to attract clients on LinkedIn, posting, etc. And because I had on my headline that I spoke Portuguese, Portuguese, Spanish and English, somebody from an agency called Merkor approached me because they were looking for Portuguese speaking writers. The pitch is you speak Portuguese. Do you want to apply for a writing analyst? Bilingual in AI. And I said, oh, yeah, having a title that says writing analysts sounds fun. What attracted me was to have the writing analyst in my top title. In reality, I always wanted to be a writer, but I thought I could never make a living being a writer, so I never tried.

[Ryan Knudsen]

Merkur told Carolina that she would be assigned projects with major AI companies and that she'd be using her skills to train their large language models. Like most Merkor contractors, Carolina signed an NDA that prevents her from revealing the name of the client she worked for.

[Carolina Perez Sands]

I thought, well, okay, okay, why not? I'm gonna be paid. The pay was not bad at all.

[Ryan Knudsen]

How much was it?

[Carolina Perez Sands]

$45 an hour.

[Ryan Knudsen]

How did you feel about that as a job to just train AI systems?

[Carolina Perez Sands]

In the beginning, I felt great because I thought to myself, oh, I'm in the cutting edge of technology. I know things that most people don't and things like that. And it was really interesting.

[Ryan Knudsen]

For contractors, being on the cutting edge of technology means trying to make the AI models good at the things they're good at. For Carolina, the task was to help it improve language and writing skills. Her first project was simple. The client asked her to evaluate how well the AI was responding in European Portuguese as opposed to Brazilian Portuguese.

[Carolina Perez Sands]

The task in the beginning was to make sure that the written responses that the models gave included phrases that were commonly said. And because I was doing European Portuguese, if the response I got had a lot of Brazilian Portuguese type of phrasing, I had to correct it. I would click on a task, and I would see the prompt and then the response, and I had to say, well, this response doesn't match the prompt well, because they were speaking with a lot of Brazilianisms. So I had to adjust and say, no, in Portugal, we don't speak like this. We say it like that, or whatever.

[Ryan Knudsen]

Beyond just catching simple language errors, Carolina also worked on projects that involved teaching AI to write and speak more like a human.

[Carolina Perez Sands]

We needed to create prompts to give us creative writing output. So if I want to write a memoir, I remember one of the prompts that I wrote was, write the opening scene for a memoir where a woman lives in Miami Beach. And it makes so obvious choices that it said, yeah, I was lying on the floor and there was sand everywhere. Like, the fact that you live in Miami beach doesn't mean that you live on the beach and there's going to

[Ryan Knudsen]

be sand in your apartment.

[Carolina Perez Sands]

Exactly. So those were the things that I needed to flag. And sometimes at the first reading, the first pass was, yeah, okay, it's not bad. And then like, wait, this doesn't make any sense. Why is she lying on the floor?

[Ryan Knudsen]

Yeah. As the projects went on and she kept training the AI to sound better, Carolina said that she and her fellow contractors got really good at catching all the cliches and patterns that the AI needed to work on.

[Carolina Perez Sands]

We had a Slack channel. There were a lot of screenwriters, playwriters, literature professors, historians. And we started to notice AI isms like, oh, AI does this all the time.

[Ryan Knudsen]

What kinds of things is an AI ism?

[Carolina Perez Sands]

At some point, everything happened on a Tuesday. So really, because they want to, they have learned In Creative Writing 101 for AI Models, they have learned that you need to write specific things and land everything in real life. So they say it was a Tuesday, and everything was a Tuesday.

[Ryan Knudsen]

The most average of days.

[Carolina Perez Sands]

Exactly. And that's exactly what I thought. Like, why would Tuesday be more average than Wednesday? It was interesting.

[Ryan Knudsen]

Our colleague Katie spoke to a lot of contractors like Carolina, and she says they have mixed feelings about this type of work.

[Katie Bindley]

Not everyone is so psyched to be doing this. I think they, in some cases, they just can't find work. And this is a way to make some money. And there also is. There was this sort of sense of inevitability that I think they all felt of like. I mean, look, the whole question of, like, how many jobs will AI automate away, super nuanced, complicated topic that nobody really debated the answer to. Right. But among these workers, they kind of feel like, okay, writing's on the wall, that at least some of the stuff I used to do is going to be automated. So, like, I'm not going to be able to prevent that. So why don't I just, like, make some money kind of on the way out?

[Ryan Knudsen]

I guess it's an interesting thing to do. I feel like as a gig, if you're out of work in that industry, because it's almost like you're burning down the building behind you, you know, like, if I can't get a job in this industry, nobody can.

[Katie Bindley]

Yeah, I mean, people I spoke with said as much.

[Ryan Knudsen]

Merkur said in a statement that training AI is becoming one of the fastest growing categories of work in the economy. The company said, quote, we are paying experts more than $3 million a day for that work. The company added that every major technology shift has looked like a jobs crisis, but the consistent pattern is that it creates new categories of work. Coming up, how some of Mercour's tactics have landed it in hot water.

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[Carolina Perez Sands]

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[Ryan Knudsen]

One thing that happens at Mercur that can make contractors uncomfortable is that they're sometimes asked to share prior work. The trouble is, much of their prior work could technically be owned by a previous employer.

[Katie Bindley]

Their responsibility to the client is to be helping to make these models capable of performing more sophisticated work. And that in order to do that in that sense, what you need is sort of people with prior work materials. The issue there is that there is a ton of prior work material people have, but they don't tend to own the rights to it. And so then you get into some of the stuff that I reported on where Mercour was approaching. In this case it was visual effects artists. They were messaging them on LinkedIn and saying like hey, we're looking to purchase high end production work such as quote 4D physics scenes with camera data depth and motion point tracking. And the guys I spoke with who had been approached were very taken aback by this request because even though they're contractors, the work they had done was for major studios. They said they sign NDAs that are like the thickness of a book and everything's under NDA. It's all protected ip. So they were very taken aback by Mercourt offering to purchase this data and work product because they were like, dude, we definitely don't own this stuff.

[Ryan Knudsen]

What does Merkur said about this?

[Katie Bindley]

So Mercour said that they do not buy intellectual property and that they license content directly from individuals and only when they own it. They also said that they do not ask for nor do they want material owned by a current or past employer. So they're saying we only want stuff that people actually have the rights to license us.

[Ryan Knudsen]

Another issue came to light after Mercor was hit by a big data breach.

[Katie Bindley]

They suffered a large data breach recently, which to be fair, many, many, many startups suffered a breach around the same time.

[Ryan Knudsen]

After the breach, Merkor was hit by at least seven class action lawsuits. They allege that Mercor exposed contractor information ranging from recorded job interviews to facial biometric data and screenshots of workers computers.

[Katie Bindley]

Within the claims of the suits were a lot of really interesting allegations about the ways in which the company allegedly acquires the data that it uses to serve its customers. So for one thing this is sort of interesting is that the contractors their computers, while they're doing their work, Mercour has software that takes screenshots of their work. And so among the allegations were that those screenshots were shared with the clients and sort of part of the data that they were handing over to the clients.

[Ryan Knudsen]

So essentially like watching the employees work and seeing what they're doing on the computers is a form of data that's being used to train AI potentially.

[Katie Bindley]

That's what the allegations are in the suit. And you know, Mercour makes the point that like we tell people that you are only to be working on your Mercur related projects when you're billing for time. So like if you have two monitors, you should only have your Mercur related work up on either screen at any time. Mercour said they strongly dispute the speculative claims in these lawsuits and they look forward to presenting the facts at the appropriate time and place. And they also added that they take the privacy of their customers, contractors, employees and those they interview seriously and comply with relevant laws and regulations. Yeah, so that was, that was their response to some of the reporting and the allegations included in these claims.

[Ryan Knudsen]

Still, Merkor is continuing to attract contractors who want to get paid to train AI. Last year the company hired 30,000 of them. And it's also attracting a lot of investors too.

[Katie Bindley]

The company, they raised quite a bit of money and it was in the fall that they had a new funding deal that valued them at $10 billion. And that actually was five times what they'd been valued the February prior to that raise. So they been one of those sort of, you know, quickly rising companies.

[Ryan Knudsen]

The startup also has competitors now like Handshake, AI Micro One and Surge that connect AI companies with contractors looking for AI training gigs. How long do you think companies like this will need to exist though? I mean, isn't the whole point to train AI so that it's capable of doing everything? So what won't theoretically Merkor succeed so much that it no longer has a reason to exist.

[Katie Bindley]

I mean, I think that these are big, important questions. There's so much uncertainty with respect to what AI's capabilities will be, what will the cost limitations be, the resource limitations. I mean, there's. I think, sort of my takeaway is that the ambitions of the companies are limitless.

[Ryan Knudsen]

This is basically what happened at a smaller scale to Carolina. She only worked at Mercur for about a year after her first assignment, that one where she had to flag Brazilian versus European Portuguese. She was surprised at how fast the AI was learning.

[Carolina Perez Sands]

That's when I worked myself out of a job because the model learned so fast. So with all the data that we were inputting, like in a week's time, you didn't have to correct anything. The prompt and the response were perfect, nothing too correct.

[Ryan Knudsen]

And so you look at it and be like, oh, yeah, that's pretty good.

[Carolina Perez Sands]

That's pretty good. But on the other side, I didn't have a job anymore.

[Ryan Knudsen]

After a few more projects with Merkor, she started to have more negative feelings about the work and about AI itself.

[Carolina Perez Sands]

I started to think my job is actually making this more of a monster. We're, like, just putting out pollution out there and what is it doing to the culture, to the society? I thought it was really like a waste of talent, a waste of time, not for me, but a waste of collective time. Like, we're not connecting people to people. So, yeah, I started to feel very, like, disillusioned.

[Ryan Knudsen]

Tell me about when you made the decision to quit.

[Carolina Perez Sands]

Well, actually, because. So this is the thing. I needed the money and I decided to quit when the money was really like the last project I did. Instead of $45 an hour, they would pay $35 an hour. And then the very last one that I did, they were paying by the task, and the pay was something around $20 per task. And I thought, this is ridiculous. So I did five tasks and I quit because I didn't think it was worth it.

[Ryan Knudsen]

Recently, Carolina got a new job. She now goes door to door talking to humans as a salesperson. It's still an open question as to whether AI will be capable of doing people's jobs and to what degree. But what this story tells me is that these AI companies are coming for you. Like, they're trying.

[Katie Bindley]

Yeah, totally.

[Ryan Knudsen]

You know, whether or not they're going to succeed or not is an open question, but, like, they're trying to make AI good enough to do it.

[Katie Bindley]

Yeah, no, I absolutely. That, you know, that that's definitely was one of my takeaways. As I've been reporting on this and talking to all these people about the type of work they do, I don't. I don't really see evidence that they're like, well, no, we shouldn't automate that type of white collar work. It feels pretty sweeping.

[Ryan Knudsen]

That's all for today. Thursday, June 4th. The Journal is a co production of Spotify and the Wall Street Journal. Additional reporting in this episode by Angel Au Young. Thanks for listening. See you tomorrow.