Ep 823: How AI Is Disrupting Entry Level Jobs

0:00AI is disrupting entry-level jobs, and in tech, the change is already well-advanced. Junior developers used to learn the job by writing the routine code that AI tools now write for them. If that traditional first step on the career ladder is gone, where does a career actually begin? Keep listening to find out. Support for this podcast is provided by Proof, the identity platform built for hiring. You've probably heard that by 2028, one in four job applicants is predicted to be fake. In fact, you've probably already seen that in your recruiting pipeline. Today, scammers are sending one person to get the offer and a totally different person to start the job.

0:46But most identity tools either only screen an application or verify a candidate's ID. Proof does both. integrated directly with applicant tracking systems like Leather from the first interview through to onboarding. So you have proof that the person you interview is the same real person you hire. You can learn more by going to proof.com. That's P-R-O-O-F dot com.

1:16There's been more of scientific discovery, more of technical advancement and material progress in your lifetime and mine than in all the ages of history.

1:30Hi there, welcome to episode 823 of Recruiting Future with me, Matt Alder. In software development, AI tools are now writing much of the code, and at a growing number of organisations, junior developers are no longer doing that work themselves. Tech is where AI's impact on first jobs is clearest, and a likely preview of what other industries will see next. When the tools handle the technical work, what's left requires judgment, collaboration, and communication. Capabilities that take much longer to develop than tech skills. That changes how people are prepared for work, and what employers should be hiring for.

2:12So, what should the pathway into a tech career now look like? My guest this week is Ruben Ogbonna, co-founder and executive director of the Marcy Lab School, a one-year alternative to college in Brooklyn that prepares young adults for careers in tech. In our conversation, he explains why the traditional college model is falling behind, what people are still needed for when AI is writing the code, and how employers should be rethinking entry-level hiring. Hi, Ruben, and welcome to the podcast. Matt, it's a pleasure to meet you. Thank you for having me. An absolute pleasure to have you on the show.

2:48Please could you introduce yourself and tell us what you do? My name is Ruben Akbana. I'm the co-founder and executive director of the Marcy Lab School based here in Brooklyn, New York. The Marcy Lab School is a financially accessible alternative to college for students who are looking for a post-secondary education and a fast track to a role in the tech sector. We serve students primarily ages 18 to 24 and are looking to make an impact into what feel like societal challenges. One, the fast-raising price of college and how, therefore, inaccessible it is to young adults. And then, two, just the fast-changing nature of not just the tech ecosystem and knowledge work in general and the need for post-secondary learning to be more adaptive and more innovative to meet the needs of the economy today.

3:38So our students have gone off from the Marcy Lab School, our full-time one-year fellowship, to engineering and analyst roles at companies like Google and Apple and J.P. Morgan and the New York Times, Spotify, Pinterest, and the list goes on. So we're proud to be building a program that we think is the future of higher education. It's such a disruptive area at the moment, isn't it, in terms of sort of early careers? There's a lot of anxiety around about AI by taking away entry-level work? I mean, what are you seeing happening to first jobs and the people trying to get them? What's going on in the landscape at the moment?

4:13Yeah, well, you know, let's just zoom out for a moment and think about what an entry-level job means just a year ago before, you know, AI had completely changed the narrative about what entry-level work could be in the future. I say that because if we look at this moment in context, we can see that we are already in a trajectory of what has been defined as entry-level roles, being kind of an increasing bar for rigor and complexity. Think about an entry-level role, I don't know, 40 years ago. You might be in the mailroom. You might be transcribing things on a typewriter. You might be fetching coffee.

4:46You think about an entry-level, like the most junior person on the marketing team. They're putting together complex decks. They're building content. They're using Microsoft Excel to do complex data analysis. They're reaching thousands, more tens of thousands of potential customers. And so in many ways, this AI evolution is just a continuation of that. The entry-level job is not going away, but the bar for what entry-level employees expect to accomplish is significantly rising. How is that evolving? What are the expectations around someone who is now taking their first role? Because as you say, things have moved on dramatically.

5:23This is definitely a step function change. This is a significant deviation from what the expectations of an entry-level role have been with the advent of the kind of rapid advancement of AI. When you think about the traditional entry-level role pre-AI, it is the person who is implementing a function or implementing a task. If you were a software developer, if you were implementing some relatively low-level or entry-level operation, whether that be writing tests to ensure that software is working or building more simple components on the visual front end of a web application, the drop-down bar, the menu.

6:01you. If you were a, again, if you were a marketing intern or an entry-level marketing person, you might be putting together a pitch presentation decks, or you might be drafting copy for advertisements that may be going out. And what seems like the trend that is emerging is entry-level folks are now expected to leapfrog that really essential point in someone's career to be able to manage in AI to do those tasks. And so essentially young people are now being asked as an entry-level employee to essentially be what was previously a mid-level manager. The skill is no longer building the PowerPoint deck or writing the test, but it's managing in AI to do those things and using your own judgment and discernment to ensure that those things are being done in a way that meets the business meet.

6:51As an educator and building curriculums and things like that, With things moving so fast, it must be really challenging to keep your curriculum current. I mean, how do you do that? How do you keep up with the way that things are moving at the moment and those expectations changing for those entry-level jobs? It is a challenge. And I think it's a challenge that is incumbent upon every single education institute, so not just higher ed, to be able to contend with in this moment. I think there is a natural pressure or hesitancy for particularly higher education institutions to adapt and evolve. A typical response is to say, well, you know, we're college.

7:30We teach the foundational skills of learning how to learn. And we teach the foundations of these disciplines that, you know, will last you as the industry changes. So we're not going to adapt, but we're going to trust that if we continue to teach what we always have, you will be able to adapt once you get out into the field. And that feels a bit negligent for today's world. We have to be in conversation with employers. We have to be in conversation with graduates of our institutions to get a sense for how they're faring as they transition into the working world and as they navigate upwards. And especially at a time like this, every year, every six months, we need to be looking at curricula and determining how we can iteratively change to keep pace with this moment.

8:13You mentioned me being an educator myself and my co-founder, Maya Betacharji. We founded the Marcy Lab School in 2019, both of us previously having backgrounds in education and school leadership. One of the first things you learn as a first year teacher is this beautiful framework called Bloom's Taxonomy. It's almost like the food pyramid of teaching. and the idea is that it kind of lays out a series of kind of like educational attainments with the bottom being essentially just like recall with the top of that pyramid being creation and evaluation and the idea is that teachers are charged with like moving students further and further up that taxonomy as a you know a way to reflect a deeper mastery and understanding of the content that you're teaching.

9:02And so it used to be the case that those top level tiers of creation and evaluation, those were the nice to haves. It was the holy grail for teachers to be able to say that we've moved our classroom to the place we're doing that. Now it's a non-negotiable. When I think about the work that we're doing in data analytics and computer science and software development, the lower rungs of that taxonomy are essentially asking, can a student replicate a process? Can they build a thing? And in this world where Cloud Code and Cursor and all the AI tools are going to be making it so that the implementation is no longer a student's concern, they have to be able to look at multiple implementations of the same thing, identify trade-offs, and determine which of the two things, neither of which are wrong, by the way, are going to best meet a business need and which risk each of them will pose in the long run.

9:57It's a hard job as a teacher, but it's a challenge that we have to rise to. I think that's really interesting. And following on from that, we sort of always talk about technical ability and skills, but you're kind of highlighting there the broader skills that are kind of now needed because work is changing so much. What else is it that students need and why are these things so important? It used to be the case that the critics or the Bears case against AI was that the tools just weren't that good or that they would hallucinate often or that you needed to frequently check over their work. And yes, that is still the case.

10:34But I think as we have seen over the past, not even just a year, but the past six months, the tools are getting better and better at a rapid rate. And what that means is that it's not just about being an error catcher. It is more and more akin to managing a really high functioning junior or mid-level person to do a task. And therefore, these non-technical skills like taste and judgment, management and supervision, collaboration and communication, those are very much human skills. And unfortunately, those human skills take a lot longer to develop and they take a lot more intentionality than some of the kind of more binary, observable, technical skills.

11:18And they're also messier. Like you can only develop them in practice. It means the traditional college model of the majority of your learning being in lecture halls and the majority of your assessment being in problems that's in exams. That just won't work anymore. And we have to be mimicking real world experiences if we want our students to be able to develop these competencies. Support for this podcast comes from Pebble. I know that many of you listening are hiring internationally and you'll be all too familiar with the wall of paperwork, rules and approvals. and you know just how much this red tape can slow things down.

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12:38Go to hipebble.ai before it's gone. That's H-I-P-E-B-L dot A-I. Terms and conditions apply. Just to dive a little bit back towards the technical side of things, and you were saying there about things like Claude Code getting better and better and better. Is there an assumption that people are now using AI to write this code? So, I mean, is there a difference between someone who can produce output with these tools and someone who knows whether the output is good? Where do the coding skills sit in this new world? We've been tracking this for some time. And again, because we view it as our responsibility to be in frequent communication and collaboration with our employer partners to inform our curriculum, you know, we've had a number of conversations over the past couple of years.

13:28And there are a couple of questions that I ask each time to see the trends. One of them is what percentage of your code that's being committed to your code bases have been written by individuals versus their AI agents? And it's so interesting to see how that number has changed. Of course, in the early days, our startup partners were moving really fast with these tools. But our enterprise clients had to exercise a lot more restraint. And then over time, whether they be some of the country's largest banks or insurance companies to public sector government agencies, I'm just hearing more and more that the default is trending towards people aren't writing the code anymore.

14:14They're prompting agents to do that. And so then the question becomes, OK, well, if people were previously writing the code and now agents are doing that, does that job go away? And one of the things that I think a lot about and reading a lot about is the difference between a task and a job. and I think we often kind of equate the two where they're different. The task is writing code, but really the job is producing software. And so the idea is if you are no longer writing code and an agent is doing that for you, what is now the role of the person who is overseeing the job of writing software?

14:57Well, the analogy that I always use is, you know, we all have like peers, friends, collaborators at work. Most of us are using, you know, Claude or ChatGPT to write emails and to write presentations and to write memos. and most of us have had the experience of working with a colleague or getting something from a friend where you can just tell that this is AI. And the issue isn't that you can tell that it's AI and using AI is inherently bad. The issue is that you can tell that it's AI because it's missing something. It's not quite connecting with you in the way that you would expect if someone wrote that into yourself.

15:35And software is very analogous to English. And in the same way that there are certain signals. Developers use the word smell, like there's a smell to it. And again, it's not about kind of deceiving an end user just for the sake of it being human, but that smell is indicating that there's something about the way this code is written that might not fully meet the problem at hand, or that might pose some risk, or that might kind of have some kind of hidden landmines in there, or that simply isn't designed to meet the need of the end user. And so it becomes incumbent upon people who are training folks to enter into the tech sector, not just about writing code, but building products and building presentation design.

16:19Yes, you may not have your hands on the files in the way that you once were, but having an understanding of what problems you're trying to solve in different implementations so that you can guide that agent to meet the need is still a very tall task. And therefore, we're still going to need a ton of talented technologists to deploy these tools. There are lots of people listening who are doing entry-level hiring or designing programs around entry-level hiring. What advice would you give to employers right now in terms of that and what they should be doing differently? The first is embracing it.

16:57We're often in spaces with our partners who are in talent and HR. And I have a ton of empathy for you all who are in the midst of an organizational transition and have to be the people on the front lines determining what to do with all of this. I think the reality is for most institutions, most organizations, it will be the expectation that you're leveraging AI in your job. And so to the extent that it's possible, How do you pose open-ended questions and tasks in the interview process that allow people, that allow candidates to demonstrate their fluency with these tools? The thing about AI is like, it's like, you know, it's like imagine having an intern who was infinitely smart but had zero restraint.

17:54you can imagine having that infinitely smart productive intern and still not being able to kind of meet the goals of the KPIs of your department in fact you can imagine yourself going off on a bunch of tangents and side quests and so you know the question is like how do we go towards an interview process or a selection process that poses more open-ended real world grounded problems to candidates, allowing them, ideally creating the container for them to leverage AI to post solutions, and then using that as a way to get underneath their thinking process and let that guide the interview, as opposed to the traditional channels of, you know, just falling back on merit and elite degrees and GPAs and all those things certainly matter, but clearly aren't as reflective of what the working world looks like today.

18:47And as a final question for you, where is this all going? If we sort of look out two or three years ahead, how is that path into a career going to change? And what does it mean for how we're going to bring people into the workforce? It's an uncertain and unstable and therefore a bit of a scary time right now. And there are a number of reasons for that. But one of the things I think about is it's a bit of a shame that this particular inflection point in AI happens to coincide with this particular moment in the economy and in the world. because of that, because there is so much like kind of fundamental instability in the economy, the narrative around AI has been one of job disruption.

19:34How do we use AI to cut costs? But every other technological revolution that looks like this, and of course this is, you know, a beast of its own, but every other technological revolution that we've had, it's been viewed through the lens of like, how do we then enhance productivity? And so I say that because for the near term, the conversation will be about job displacement and cutting costs because that's the mandate of companies right now because free cash flows crunch, interest rates are bad, and there's a war going on, and all the reasons why a company would need to hunker down. But the reality is this is going to drive demand for software in a way that we've never seen before.

20:18It's going to drive demand for productivity in a way that we have never seen before. This is going to drive demand for design, accounting, and ways that we can't fathom. And I think about what it looks like to prepare and create pathways for entry-level talent, for early career talent to come into organizations, both small and large, and transform operating systems. I think about how much more productive individuals will be able to be to an organization. The example that I always use is the Marcy Lab School is a relatively small team. We're a 30-person team. We're six years old in terms of operating budget.

21:05We operate on a$7 million budget a year. Under no circumstances pre-2024 would it ever make sense for a nonprofit organization of our size to think about building out a tech or IT department. because you need a certain amount of headcount and a certain level of seniority to be able to produce at the right level. In 2026, it would be incredibly advantageous for an organization like the Marcy Lab School to hire one or two technologists and the amount of software and tooling and solutions they will be able to provide for the organization would be transformative. And the only reason why we're not talking about Marcy adding jobs for this moment for people who can leverage AI to solve problems is because we're in a tougher fundraising environment than we were three years ago because of the economy.

21:53But that time will come. And so I think organizations and companies need to be thinking right now about new roles and new job descriptions that don't currently exist for people who can come in and leverage AI to solve really big problems. Ruben, thank you very much for talking to me. Matt, I really appreciate it. Thank you. My thanks to Ruben. You can follow this podcast on Apple Podcasts, on Spotify, or wherever you listen to your podcasts. You can search all the past episodes at recruitingfuture.com. On that site, you can also subscribe to our weekly newsletter, Recruiting Future Feast, and get the inside track on everything that's coming up on the show.

22:31Thanks very much for listening. I'll be back next time, and I hope you'll join me.

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