Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 636: Uber paying drivers $1 to train AI models? A sign of what’s next

Episode Date: October 21, 2025

Uber is paying its drivers as little as $1 to train LLMs. 😯Smart business move or eery sign of what's to come? On this Hot Take Tuesday episode, we uncover the trend of dirt cheap data labeling, why it's a good thing and a bad thing, and how this is actually a sign of what's next. Uber paying drivers $1 to train AI models? A sign of what’s next -- An Everyday AI chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Uber Digital Task Program OverviewUber Paying Drivers $1 for AI TrainingAI Data Labeling and Microtask EconomicsImpact on Gig Worker Job SecurityHuman Data Training for AI Model QualityAI Model Collapse and Internet Data ExhaustionFuture of Nine-to-Five Work and AIEnterprise Strategies for First-Party Data CollectionUniversities and AI Company Partnerships PredictionTimestamps:00:00 Uber Drivers Training AI Models05:03 Uber Drivers Completing Digital Tasks07:11 AI Workforce Shift12:17 Changing Future of 9-to-514:13 AI's Mixed Impact on Jobs18:39 AI Competition and Data Scraping22:37 AI Content Crisis Explained26:02 Model Collapse and AI Plateau28:24 Reddit: AI's Human Data Source33:39 AI, Data, and Future BlueprintKeywords:Uber digital task program, Uber paying drivers to train AI, AI model training, $1 micro tasks, Large Language Models, AI data labeling, crowdsourcing human data, gig workers, AI gig economy, job automation, job replacement by AI, AI impact on workforce, autonomous vehicles, computer vision, Waymo, driverless cars, AI-powered ride sharing, AI data collection, first party data, AI data scarcity, synthetic data, AI regurgitation, model collapse, reinforcement learning with human feedback, incremental AI model improvements, knowledge cutoff, AI-generated content, dead Internet theory, bot traffic, Imperva bad bot report, Reddit data deals, Quora, AI partnerships, Fortune 1,000 companies, enterprise AI adoption, fine-tuning teams, domain specific modelsSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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Uber is paying drivers $1 per task to train large language models. And I think that this has grabbed headlines in traditional media and has really set off a lot of discourse on social media. People saying, is this a good thing or is this a bad thing? Well, I think regardless, this is actually a sign of what's next. And regardless on if your take of if this is a good thing or a bad thing, I think this is going to become very commonplace in the U.S. especially as we head into a time where the internet is essentially dead and AI labs and companies need human death.

All right. So on today's show, we're going to be talking a little bit about this. story and I'm going to give you my hot take on, yeah, get used to this. We're probably all going to be training AI models to do our jobs. All right. Let's dive into it. What's going on, y'all? Welcome to everyday AI.

My name's Stuart Moulson. I'm your host. And this thing is your daily live stream podcast and free daily news that are helping everyday business leaders like you and me, not just keep up with all these AI headlines like this, but how we can make sense of them and grab the most important information to help us make decisions that grow our companies. in our careers. If that's what you're trying to do, great.

Starts here with the unedited, unscripted, live stream podcast. But if you want to take it to the next level, that's where our website comes in. Your best friend, your everyday AI.com. Go sign up for our free daily newsletter where we recap the highlights from each and every episode as well as give you what you need to know on the AI news. So if you want the AI news, make sure to go check out today's newsletter. It should be an exciting announcement coming from Google on vibe coding inside of AI Studio, FYI. All right. But let's talk about what we're going over on today's show. Yeah, Uber paying a dollar per task for humans to train AI models. Well, I'm going to detail what Uber's new digital task program is and tell you how it work. I'm going to tell you why I think it's both a good thing and a bad thing. I'm going to preview what it means for the future of the U.S. economy and more. And I'm going to give you my hot take on actually what this means. All right.

Live stream audience, good to see you. If you have any questions, let me know. Go ahead, get them in now. But yeah, if you listen on the podcast, FYI, we do this thing live, unscripted, unedited. It's something I like to do. I think so many, you know, podcasts out there or, you know, sources of information are overly polished. And, you know, we just like to give you just the real stuff. So good morning.

Dino joining us from Italy. Jay joining us on LinkedIn from Minnesota, Brian and Marie. Good to see you all. Tim from the YouTube machine. If you have questions on this or if you want your opinion to be heard, go ahead and drop it. But here is the new digital task program overview. Yes, Uber drivers are getting paid sometimes a dollar per task to help Uber trained AI models. They swear it's not for autonomous vehicles and to replace.

the actual drivers who are completing these tasks. But I'm sharing a little screenshot here for our live stream audience showing about how it works. So the way that Uber is launching this, it's inside of their work hub. And drivers can do these tasks for sometimes as little as a dollar, sometimes more. So Uber says they're quick and easy. Each task just takes a few minutes to complete. They say you can earn money on the side. So to do these tasks offline while you're not driving, my gosh, I hope people are doing this

offline while they're not driving. And they say, no experiences needed. A phone is all you need. All right. And then drivers can log in and do these things kind of while they're not driving. So this was just announced this past week. And the digital task program pays driver small amounts to complete these micro tasks that ultimately help Uber train its own models. And they sell that data to others. It's optional, obviously, for drivers who opt in or don't opt in. But it's essentially for their Uber's current pool of millions of these gig workers who drive or maybe are doing other gig work. This has expanded now just to the U.S. after a successful pilot in India and Uber uses the data

to improve its own services, but they also sell this data to other AI labs. So here's how it works. Drivers opt in to receive invitations for these digital tasks through the the Opportunity Center in the Uber driver app. The tasks are reportedly quick and can be completed on a phone often while away from the vehicle or just while they're idling, waiting for their next ride. Payments for each test start at just a dollar, sometimes more for more complexity. And then the earnings from drivers, you know, kind of get added to their account within 24 hours.

This is normal. And I think it's actually important to have a conversation. We're not going to get into too much into the weeds. data training, but it's important to know this isn't some one-off from Uber, right? There's dozens of companies valued at billions of dollars, multiple billions of dollars apiece that this is essentially what they do. They pay sometimes low-wage workers from other countries to make sense of data and to complete tasks.

So companies like Uber, scale AI, Appen and Samma, essentially they're crowdsourcing platforms that hire workers globally to perform these micro tasks that train AI systems. And many of these jobs, we've seen some ugly headlines over the years. Well, oftentimes they're low paid, you know, and sometimes they face payment or labor law issues, especially when they're U.S.-based companies hiring workers in developing countries. Firms are, though, moving away from these large kind of pools of low-paid generalists, right? So we saw some stories back in 20, 23 and 2024. A lot of these bigger companies were paying ultimately workers a dollar or $2 an hour,

but not necessarily specialists. They were just sometimes anyone these companies could find paying them a couple of dollars an hour. So now there's been this shift door, number one, at least for the U.S. companies, bringing some of this talent abroad or sorry, bringing it domestically, bringing it in-house to the U.S. but also moving away from generalists to specialists, which is why I think it's important for all of us to hear this. All right.

And workers doing these tasks may be helping develop AI systems that could eventually automate or replace their own jobs. And I think this is going to become, unfortunately very common in the late 2020s, where you might have a role at your company, where it looks like you're helping your team better adapt to AI. You're helping collect, data internally, first party data.

And ultimately, you might just be training a model that replaces your job. And this is a much bigger conversation, right, than tackling Uber's new digital task program, but it's actually both a opportunity and a threat for traditional nine to five work. So let's look a little bit more at what this program is actually offering us. for these tasks. So the examples that Uber drivers are doing, so they may be recording voice clips in their native language, they may be uploading photos of specific items or locations into the app, and then submitting documents in various language such as restaurant menus. So again, some of these tasks, you might look at them and say, okay, this is for autonomous vehicles.

Uber wants to compete with Waymo, right? They want photos and and a certain location-based data that will help Uber better understand what it is they do. So I think part of it is true. And even though Uber says, hey, this isn't to replace jobs, I think ultimately, if this is a successful program for Uber, I think it ultimately will lead to replacement of jobs from these people that are doing it. Because that's where this industry is heading, right? We don't talk a lot about autonomous vehicles, right?

Waymo and what Tesla is attempting to do. with kind of the robo taxis. We don't talk a lot about that. The whole computer vision, autonomous vehicle industry, it's huge and it obviously has a big crossover with AI. But the future is autonomous vehicles. Sure,

we've been promised them for decades, I feel, and they're not really just becoming a reality, I feel, until 2025, where it's now commonplace, at least in certain cities, right,

whether you're in California, I think Texas is another big place, right, where it's kind of common to jump in a Waymo and have an autonomous ride from point A to point B. So what is the purpose of this program, right? I think the $1 task kind of entry point is what, rightfully so grabbed a lot of headlines, right?

Yes, these are kind of micro tasks that may only take a couple of minutes to complete. So yeah, it could be a good source of income. So I think, you know, looking at the good and the bad of it, Is it a great short-term money-making opportunity for gig workers who are already Uber drivers? Absolutely. Right. So you have to look at a time where economically things are challenging, right? I can't tell you how many recent grads, especially here in the U.S.

Just can't find jobs, right? So the reality is a lot of them in the interim are maybe taking on positions that they may not normally take on that are outside of the area that they got their degree in. Maybe because colleges weren't teaching AI and not preparing them the necessary skills, but I've already tackled that plenty here on this show. But what this has led to is a lot more gig workers, right, especially in the younger generation that are finding it harder and harder to find jobs in their area of study.

So there's good and bad to it. Good is it does provide some short-term economic, from people that are struggling to find full-time employment or those who are already, you know, doing some gig work on the side, doing some Uber driving on the side. Now there's some more opportunities that don't necessarily involve them driving, right? Things that they can do at home or, you know, between rides as an example. So short term, I think there's some good to this long term in terms of normal economic security. There's not a lot of good, right?

A lot of people think and assume, oh, well, because this Jordan guy talks about AI every single day, you know, he wants AI to take all jobs and wants all companies to be AI native, right? That's not necessarily the truth, but this is just the reality. It's not my personal opinion, but the reality, I think, is the nine to five is not going to be like it is in, you know, five or 10 years. I do think, and I've gone in depth, you know, when we kind of do our yearly prediction in roadmap series, I've said this for multiple years. Traditional nine to five work is going to eventually not be the norm anymore, right? I think especially for college educated people, the nine to five kind of career path has been the absolute norm for decades. And I don't think that's going to hold true.

So, you know, kind of little programs like this, Uber's, you know, digital task program that are paying a dollar. I think things like this are going to become increasing. more common. All right. So let's talk a little bit more about the program and then I'll get a little bit more to some of my takes. But the data collected is used by Uber AI solutions. So Uber does have a dedicated data labeling units and they do use this internally and they sell this to other AI labs in enterprise companies. But it doesn't come without its fair share of criticism. Right. So kind of how I opened the show, you've seen this on traditional media and on social media. It is kind of a polarizing topic depending on what your views are. Again, I think it's short-term economic relief, long-term economic uncertainty, right? This is, I think, will be one of the earlier kind of news stories where this concept becomes a household conversation, right, where the average person will be training AI models in a very non-technical way that eventually will lead to longer-term job displacement or replacement.

Yes, this isn't going to turn into a long rant on, you know, AI's impact on jobs. But the reality is, yes, AI will create more, you know, opportunities and full-time jobs that don't exist today. Millions, yes, but I've been on record for now almost three years saying that I think AI will have a net negative impact. on job creation in the long run. And I think in a big way, but like I've said, I think the majority, it's going to be very common for people to have multiple part time jobs or multiple businesses that they own in the future because of AI and because it's going to make it easier and even programs like this, right?

Uber's data labeling program that they're going to sell this to, uh, you know, other enterprise companies. It's going to become easier for all of us, all everyone here listening to the show, to launch your own business. Even if you aren't necessarily thinking of yourself as an entrepreneur, I think it's going to be very commonplace for this to happen. So this has launched this program, not the best timing for Uber, but it's because it's launched amid concerns and criticisms about AI automation and job

security for gig workers as autonomous vehicle tech evolves, specifically in this Uber case. But Uber does claim that this data is not going to help them create self-driving cars. But critics do note that AI data labeling has historically just been low paid work for cloud workers in the global South, raising questions about labor economics and fair play. But my hot take is this. We both need this sorely and it's absolutely terrible. All right. I delivered you the facts and the stats.

Let's get into my hot take here in a second after a quick word from our sponsors. This podcast is supported by Google. Hey folks, Stephen Johnson here, co-founder of Notebook LM. As an author, I've always been obsessed with how software could help organize ideas and make connections. So we built Notebook LM as an AI-first tool for anyone trying to make sense of complex information. upload your documents and notebook lm instantly becomes your personal expert uncovering insights and helping you brainstorm try it at notebooklm.com here's the harsh reality of where we're at with data and large language models without getting too specific and probably too boring for much of our audience here's here's the way that it works right the big a i companies, your Anthropic, Open AI, Google Microsoft, right?

They've essentially been scraping the internet for anywhere from, you know, four to eight years, specifically with the goal in mind of training large language models. So that's the open, close internet, third party data sets, even copyrighted works, right? We're seeing a lot of these lawsuits now finally pay off, right? We saw the $1.5 billion fine levied against Anthropic for reporters. training on copyrighted books. What this has led to, essentially, all of these big AI labs have kind of hit this,

this point where there's no more really unique data sets to train their models on. And everyone's playing off the same data, at least that has historically been connected. But the dead internet theory is, very real. All right. And I'm going to get into that here in a minute. But AI labs and the thousands of businesses that now rely on outputs from these AI labs sorely need unique human data that's not available anymore. It's already been scraped. It's already been ingested, regurgitated, spit out and reused, right? There's no unique data on the internet anymore.

You know, maybe, maybe, uh, you know, data will start to become a, little more unique or exclusive, right? As certain big providers, like I'm going to talk about Reddit here in a minute, but as they sign exclusive deals with certain AI labs, and once they can successfully block all the other AI labs from accessing it, right? We've already seen many big name lawsuits where essentially some of these big websites and media companies have entered into exclusive agreements with, you know, AI Frontier Company A have restricted AI Company B, AI Company B doesn't pay attention. They still scrape that website and now there's lawsuits, right? But the reality is there's, you know, back in 2023, 24, you know, there was a little

competitive advantage for these AI companies that could, number one, not to successfully scrape everything, right? All the, you know, legal and illegal ways that AI companies scrape data, but that if they had the right pre-training, if they had the right reinforcement, you know, reinforcement learning with human feedback, if they could properly take the, um, essentially entirety of the internet, entirety of human knowledge and properly train a model on that. But I think that gap has shortened to essentially zero, right? So I think earlier on in the case of especially 2023, you know, there was an advantage to be had when you had the most talented people

that could look at every single piece of human information that is scrappable and could do the best job of training a model to provide good examples, right? Good outputs. That's not a factor anymore. And right now, it's not just companies like Uber or companies that are data collection and curating companies. Individual businesses need to start thinking if you are in the C-suite and an enterprise company, right, let's just say a Fortune 1000 company. And I know there's a lot of you out there listening to this program that fit that mole. If you're a decision maker at a Fortune 1,000 company here in the U.S.

If you're not already doing something like this, you have to start. Right. And I know what this means. This does mean you're going to have employees internally that are helping with this process, that are essentially training models that are going to replace their day-to-day jobs. Yet, companies have to do this. You are not going to be able to compete in the next two to three years. if you're not already collecting first party data in this way, in this example that Uber is

having people do. The internet's dead, though, right? There's a lot of analysis that I don't necessarily agree with how they came to their conclusions. Yet the conclusions are overwhelming, right? There was a kind of two more recent studies. So one that bot traffic hit 51% last year. So essentially there's more AI bots perusing the internet than humans, which is crazy when you think it's billions.

All right. And that's per Imperva's bad bot report. Also a more recent study. Again, I don't necessarily agree with how they classified AI content versus human content. But regardless, there's been a lot of recent studies. One was a graphite analysis, I believe, that's said over half of new online content is now AI generated as of this year. There's been other studies that have projected that more than 90% of content by the end of 2026 will at least be partially AI generated. You might be wondering like, okay, Jordan, like, what's the big deal? Why does this matter? Well, okay, if more than 90% next year, if more than 90% of new content that is published on the web is somehow.

Now, AI generated or AI augmented with a human creator, this creates this regurgitated cycle of sometimes AI slot. And if you want your business to succeed, you have to be able to tell the difference between what is high quality data and what is it, right? Which is one of the reasons why Uber is even doing this in the first place, right? Why they're having who they hope are educated humans making educated decisions. right so the reddit co-fine founder recently said this so much of the internet is dead due to bots and a i slop sam altman has recently said this month said so much as well so why is this important

like i said companies have run out of training data so according to an epoch uh i research public training data could be completely exhausted by next year all right Elon musk said early this year that quote unquote, we've exhausted basically the cumulative son of human knowledge. In other words, AI models have already consumed every single piece of recorded human knowledge there is, right? Everything that's been published on the internet, videos, works of art, right? Because models are multimodal can ingest content in a multimodal fashion, offline data sets, et cetera. In Gartner, says that even in 2024, 60% of AI training data was synthetic.

Or, and this is in 2024, so we'll see what Garner releases next year. I would assume that number is probably in the mid 70s to 80%. But already, the overwhelming majority of new information hitting the internet is synthetic. It is from AI. It is made up. It is AI generated. So I hope you can see this is a problem. It's this, I call it AI regurgitation.

All right. The more sophisticated name for this is model collapse. So there was a, and we shared this in the newsletter when it first came out, a 2024 nature study proved that AI models essentially collapse when they are trained on their own outputs because that's what that's what's happening. It is a I slop regurgitated. All right. because when humans are getting lazy and let's be honest, humans are lazy. And a lot of humans look at large language models as an easy button, right?

They want to maximize time savings and put sometimes a little less effort in creating something. And if all we're doing ultimately is using AI to create more data that will be consumed by AI and trained on for the next AI model, this is what leads to model collapse. In other words, models are not going to be as smart if there isn't a shift in strategy. And that's what we are seeing here. And that's what I think this Uber example is one of the first big shifts in companies going a different direction, right? So essentially model collapse. Think of it like if you just keep photocopying a photocopy and you keep going and going and going after 10, 20, 30 iterations of photocopies. copying a photocopy, it's going to become unreadable, unusable.

And that's what we're, I think that's where we're at today. And that's why a solution, like I said, there's good and bad out of this Uber, but this is technically what we need because AI companies and I think enterprise companies desperately need fresh human data to survive. Because model quality has essentially plateaued, right? You can look at all the benchmarks. Every new model that probably costs billions. millions of dollars to train is only now seeing incremental gains over previous models.

And you might be thinking why. Well, essentially, if you look at how large language models are trained, there's something called the knowledge cutoff. So essentially, these big AI labs, it takes a while, right? So when we see, uh, you know, a GPT5 model that was released in August, I believe the model training cutoff was about a year prior, right? So it takes all of these smart people that the big AI lab, sometimes a very long time to look at their, hey, here's what we've collected, right? Here's all the data that we've

scraped or we've entered into partnerships to collect all this data. And there goes through this curation and cleaning and model training process. But by the time models, you know, quote unquote new models hit the shelves. It's already very old data, right? Sometimes the model training cutoff is about a year or more for new models. And that's why when you see all these new models release, right? Sonnet 4, 5, you know, GPT5, right? I do feel we'll, we'll see, you know, a Gemini 3.0 here, you know, pretty soon in the coming days, weeks, or months. But that's why now there's just such these small gains in a lot of these benchmarks, right, where 18 months ago you would see huge jumps. Now it's not. Competition is

now, I believe, more about features and U.X. It's not about intelligence anymore. I think that gap has closed because of the data and the model training. Right now, I think it's more about the scaffolding and the tool calling and the agentic nature than it is about the actual data that these models are trained on. Reddit is a great example. Reddit is one of the most cited, at least when large language models cite their sources, right? When they go out and go out to the web and grab new and fresh information to answer queries, which is now the D. de facto way that most large language models work.

You know, Reddit is one of the most cited or sourced pieces or authorities out there. And they make right now, reportedly, more than $130 million a year from AI deals, right? So I think it was about $60 million from Google, $70 million from OpenAI per ad week.

And that's a big chunk of Reddit money. But in Reddit has also blocked, you know, such as the internet archive and other companies, from getting their data. But why am I bringing up Reddit as an example? Reddit is, well, humans, right? Sometimes it's a great source for AI training

or for outputs, depending on what you're using a large language model for. Sometimes it's not, right? You might not, for certain business inquiries, you know, if you see Reddit as a cited source, you know, some random, you know, person in Ohio talking about, I don't know, a solution they found to a problem. Sometimes it's good.

Sometimes it's not. But human, unique human data in the case of Reddit, Quora, et cetera, is extremely valuable. So let me start to wrap up today's show and why I think Uber's $1 digital tasks are actually a sign of what's next. This is not a one-off small story that everyone's going to forget about. I think this is going to become the norm in the coming years. And here's why. And I've been saying this for a long time. In 2023, I said full time, nine to five work is going to become a thing of the past pretty soon.

People thought I was crazy until LinkedIn CEO Reid Hoffman said the same thing a year later. And I do think that many jobs in the latter part of this decade will be kind of similar to what we're seeing now from this Uber $1 digital task. Your full time job may in two years be. training a model in a non-technical way. I think there's actually a huge, a huge business opportunity to bring kind of the UI or Ux of what Uber is doing to companies, right? Non-technical ways to get your skilled humans to train models for your own company's use. And I also said this in 24 and again more deeply this summer. Companies, like I said, many companies already doing this, the big Fortune 100s, but I think this is for enterprise companies in the U.S., this is going to

become the norm. You are going to have a whole department pretty soon doing similar tasks, right, that are obviously domain specific, category specific for your company. But you are going to essentially have fine-tuning teams, right, that are going to have. have to take work with these large data sets and essentially train models. And I do think small, you know, small categorical or small niche models are the future for Enterprise. I've been saying that for a long time as well. But I think this is going to become commonplace to have teams of people working with

first company data. It's going to be huge. And also, I think colleges, let's be real. Enrollments already starting to decline. It's going to hit a cliff. probably here in two years. Companies, or sorry, colleges and universities are struggling financially because enrollment is going to start to dip.

And because for the last three years, colleges and universities have essentially stuck their head in the sand when it comes to preparing students for the real world because companies want AI skills. Universities aren't teaching this. It's going to be a huge backlash. I already did a full episode on this. But essentially, I think you're going to have these big AI companies, aqua hiring universities, right? Universities are going in the same way, right? I'm a big North

Carolina basketball fan and North Carolina is kind of sponsored by Jordan Brand. Right. So they're, you know, a jumpman athletic department. I think the same thing is going to be true for universities as a whole, especially those universities that aren't thriving right now and are facing enrollment problems. They're essentially going to have to take on investments from AI companies or to essentially get aqua hired by big AI companies or data companies in order to survive. But I think that there's a huge source of human content, right? You need, right, like what happens in lecture halls, you know, amongst doctoral students debating certain, certain issues of today, you know, trying to explore new scientific

breakthroughs, there's so much unique data happening, both inside companies and inside universities, which is why I think this $1 digital task from Uber is just a sign of what's next. And it is going to start to unfold, maybe not tomorrow, maybe not next month, maybe not next quarter. But I can guarantee you, in 2026, we are going to see stories like this from companies, from universities, it is going to be the norm. So if you are a business leader thinking of what is the best way to use and implement AI within your organization, I think this Uber example is both absolutely terrible and absolutely crucial to understanding the future of how to work with unique human created data when AI models

and the training data is essentially just going to become and has become regurgitated AI slop. So this is kind of a blueprint for succeeding and getting ahead in the era of the dead internet. All right. I hope this was helpful, y'all. If it was, tell someone about it. We put in a lot of work to bring you usually unbiased, right? This is a little hot take Tuesday episode talking about a recent news piece and giving you my kind of hot take.

But we put a lot of work into everyday AI. So you can have a place for non-technical business leaders to come and just get the no BS version of what's happening in the world of AI. So if this is helpful, please, if you're listening on Apple Podcasts on Spotify, please subscribe and like the show. And if you haven't already, tell someone about it, share about this, repost this if you're listening live on LinkedIn. And then when you're done, the most important step, please go to your everyday AI.com. Sign up for the free daily newsletter. If you miss anything, if you need to hear more on this, we're going to be recapping the highlights in today's free daily newsletter, as well as keeping you up to date and making you the smartest person in AI at your department or your company.

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