You Ask, I Answer: What Jobs Will AI Create?

You Ask, I Answer: What Jobs Will AI Create?

In today’s episode, we’ll explore the surprising new jobs that AI will create. You’ll learn about the emerging markets fueled by AI’s unique problems. Discover how AI’s limitations are opening doors to lucrative opportunities. Get ready to identify the potential for your own AI-powered career path.

You Ask, I Answer: What Jobs Will AI Create?

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

Christopher Penn: In today’s episode, Mark asks, What jobs will AI create? So this is something that has been obviously debated very heavily, we know that AI is going to consume a lot of jobs.

So a bunch of folks on LinkedIn talking about how the old saw that we’ve said for years now that you know, a person skilled with AI will take your job not AI itself.

And depending on your job, that’s no longer true, right? If your job is a series of single task jobs that machines can do, yes, a machine will take away the components of that job until that job is no longer with worth employing.

However, the flip side is what jobs will AI create? The answer is, we don’t know, it hasn’t happened yet.

But what we do know what is very clear is that just as AI saves a lot of time and effort, it also consumes a lot of consumes a lot of resources, it consumes enormous amounts of electricity, for example.

So all the things that happen in an electric supply chain, you need more of it.

Where do we get more electricity, Microsoft was saying that they’re looking at strapping a nuclear reactor to one of their data centers, because they just can’t get enough power.

Obviously, the more power you’re generating, the more infrastructure you need to do that and more jobs are in that particular sector.

We know that AI is running out of training data, there was a piece in the New York Times, just Christopher Penn: yesterday, about how AI companies were basically grabbing every piece of data they could possibly get ahold of to train today’s large language models and ignoring ethics and intellectual property as it just vacuumed up everything, which, again, is no surprise to anyone who’s worked in the field for more than two minutes.

But what companies like you know, the French company Mistral, which makes the Mistral family models clearly demonstrated with their models.

Just because you got a lot of data doesn’t mean that Christopher Penn: it’s good.

And a model that’s trained on everything instead of just the good stuff underperforms a model trained just on the good stuff.

Here’s the challenge.

The challenge is, there isn’t enough good stuff.

Right? Think about a power law curve, right? Which is like sort of the opposite of bell curve, you have a short head and a long tail.

In a power law curve, the short head is quality content, the long tail is not quality content.

And the internet is full of content, but a lot of it’s crap, right? A lot of it’s not very useful.

Even if it’s well written, even if it’s good, in general, it may not be good for your purpose, right? Your drunk uncle’s Reddit shit posts are probably not good for anything.

But your blog about B2B marketing, probably a very good blog.

My blog about generative AI, I would like to think it’s a good blog.

Is that content helpful if you’re training a model on medical diagnostics? No, no, it’s not.

Christopher Penn: It doesn’t really offer anything beyond basic word associations.

And so one of the nascent opportunities that appears to be coming up is companies hiring humans who are qualified humans to write more good content.

A friend of mine who is a PhD in a very specific field, AI companies paying them 50 bucks per per piece of content, just to give them training data.

And it’s laborious, Christopher Penn: right? Christopher Penn: It requires their domain expertise, their domain knowledge to to train this model.

And so they have to sit down and pound out 750 words at a time and get paid, you know, decent money for it.

It’s not great money, but it’s decent money.

It’s certainly something that they can do in their spare time.

But that’s one of the tasks that machines need machines just need more good content.

And so one of the career paths, at least in the short term, we’re probably talking, you know, next Christopher Penn: two to five years is getting more expert content, more high quality content into training libraries and training data sets that can then be resold to AI companies, it would not surprise me in the slightest to see consortiums of companies, you know, hiring freelance photographers, like, hey, we need 1000 photos of passenger cars, we need 1000 photos of SUVs, because we’re helping create a labeled training data set.

For SUVs, we need 1000 photos of milk cartons, right, and someone’s gonna go out and gather up this data and create the data, because it doesn’t exist yet, at least not in the format that that high quality modelers want.

And so that is already an indicator that supply chains are shifting.


So if you want a model to generate milk carton identification, you need a lot of that training.

data, and it doesn’t exist.

So there has to someone has to make it.

And that someone could be you, that could be your company, you if you have access to data, we have access to a talent pool of people who can create commissioned types of data, there may be a real market opportunity for you.

Other things that we we just don’t know.

There are certainly, you know, prompt engineering itself, Christopher Penn: is simultaneously becoming less and more important is less important for big general models.

It is more important for small open weights models where the model performance can really be made or made or broken based on the prompt.

But even if the for the larger models, there’s a strong call for prompt engineering for within a company.

So that company may bring someone and say, we need Christopher Penn: 10 prompts for HR, we need 10 prompts for sales, we need, you know, so on and so forth.

And that is something that if you have those skills, you may be able to go into a company and say, Hey, let me help you get get rolling quickly.

With these tools.

There is an enormous amount of concern, which is valid about the safety and security of language models and the data that feeds them and the data that they produce.

Anytime is like anytime.

This is something I got from my friend Chris broken anytime there’s an opportunity to be helpful, there’s an opportunity to earn money.

Anytime someone’s got a problem, there’s an opportunity to be helpful.

And if there’s in a corporate sense, in an organizational sense, if there’s a problem, there’s an opportunity for someone to make some money there.

So if a company identifies that cybersecurity is a real problem now with dealing with language models that can code autonomously, there is an industry now for people helping defend systems against those types of organized attacks.

If Christopher Penn: there’s a problem with misinformation that is causing troubles at a company there is there is a market space for solution.

So one of the easiest ways to think about what jobs AI is going to create is look at what the new problems are.

What are the new problems that don’t have solutions yet? Can you build a solution? Whether it’s, you know, just a couple little things or a full enterprise sized company doesn’t matter.

If you can identify the problem, you can, you can create the solution for it.

And if you’re early enough, you might be the solution provider for it.

So that’s the short answer to the question, what jobs will AI create? Any ill create jobs to solve the problems that AI creates? So as you think about the problems that AI is creating deep fakes, and this and that? Are you thinking about the market? opportunity to create a solution for it? That’s the episode that that is it for this episode.

Thanks for tuning in.

I’ll talk to you next time.

Definitely tells us not AI generated because that script wouldn’t happen.

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