The AI engineer skills gap

Welcome to the Practical AI Podcast where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work and create. Our goal is to help make AI technology practical, productive and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn X or Bluesky to stay up to date with episode drops behind the scenes and AI insights. You can learn more at PracticalAI FM. Now onto the show.

Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I am CEO at Prediction Guard and I'm joined as always by my co host Chris Benson, who is a principal AI research engineer at Lockheed Martin. How you doing, Chris?

Hey, doing very well today, Daniel. How's it going?

It's going really well because have a close friend joining us on the podcast today and a previous guest. We went through the Intel Ignite accelerator program together in different companies and yeah, just really excited to have with us Ramin Mohammadi with us who is an adjunct professor at Northeastern University and also lead principal AI engineer at ibaset. Welcome, Ramin, it's good to see you again. Yeah, thanks Dan.

Chris, it's always great to be back.

Yeah, yeah, I've been excited to talk through these things and even before the show, obviously you're kind of living in two worlds. You're living in the industry world and you're living in the academic world and you've kind of been living in those two worlds for quite some time, which is interesting because you have a perspective on like how for example, data scientists or AI people or machine learning people are being trained and what those people are actually doing in industry, which I find really intriguing, especially because so much has changed. I guess maybe that's a good initial question is, is my perception, right, that like the role of an AI person or a data scientist or a machine learning person in industry, like that the day to day life of that person has really changed dramatically over the past even few years and I'm curious if the academic side has kept up with that.

Yeah, so I think that that's an interesting question. I think we need to break it down into multiple sections because I mean, let's just start first do a quick review of what has happened, you know, because we're talking about the complete transformation of the AI and data science job market. You know, I mean if you remember, and it was about like a decade ago, back in 2012, Harvard Business Review, they Called data scientists the sexiest job of 21st century.

Yeah, that's why I got into it because obviously that describes what I want it to be.

And if you think about it, that, that one phrase, it kicked off a massive gold rush. Everyone wanted it. Universities were spinning up the new master programs overnight. And the promise was pretty simple. Get a degree and learn a little bit of machine learning and you also, you're instantly employable. That promise feels like almost like a myth now. You know, I mean, if you talk with any new graduate today, especially someone looking for the first role, the feeling, it's totally different. It's brutal. The market's absolutely brutal. We see job posting for entry level. You know that that job requires about three years of experience. The demand has changed. It's shifted fundamentally. It's not about what do you know about from the textbook anymore, it's about what can you build, can you deploy and maintain a real, like a scalable AI system? It's kind of like that's the new currency of hiring.

I think one time, Chris, I don't know if this was, if this was us that came up with this discussion, but I remember quite a while ago we talked about kind of like full stack data scientists or something like that. The idea being you could figure out what kind of modeling you needed to do. You could do the prototyping and PoC, but you could also deploy something to actual cloud environments or something like that. I mean that seems like quite a tall order, Ramin, because you're basically saying be a software, like a proficient software engineer, but also be an infrastructure person. And also, and there's this, I don't know, I've heard a lot of people say there's not really like a full stack engineer doesn't really exist. So yeah, I guess from that perspective, how much of what a data scientist or machine learning or AI person fits into those different buckets at this point, Whether it's software engineering or infrastructure work or actual like knowledge of differential equations or statistics or something.

I think that's also a great point. So if you think about back to data science job, the idea of data science job was that your job is kind of done once you got a good score in the notebook. You know that the classic my model has 95% accuracy on the test data and you're good, you pass it to someone else. And then if you remember, I think it's around 2000s with some resources like Google Cloud, rules of MLOps. It laid out these new realities that successful ML needs a home Suite of real engineering skills, the things like containerization with Docker CI, cd, pipeline automation monitoring. And you have to know if that your model actually works in the real life and then you need to monitor it and after you deploy it, you need to basically look for the drifts, you know. So industry made it really clear that job wasn't just build the model anymore. It's kind of like you need to own the pipeline. So and then if you think about it, all of a sudden the analysts or data scientists went from just being a simple analyst to be on engineers who build and maintain the intelligent system. And so just as that engineering bar was being raised by MLOps, along comes the second, maybe even bigger tidal wave, the generative AI. And that becomes like around 2023, explosion that you can see in the Stanford AI index. Basically they mentioned that this was not just a cool new tool, this was an automation event. It immediately attacked the entry point in the field that they could do those jobs. Basically, you know, it's kind of. So this shift was drastic from the data scientists to MLOps engineers and all of a sudden AI, basically.

In addition to that, there's so much more diversity in, you know, as we were talking a moment ago about the notion of the full stack engineer, especially at the entry level, trying to fit into this and the notion of like what is full stack is changing fairly rapidly. There are a lot of different options out there. And not only do you have to try would that entry level student have to try to fit in to the notion of what an organization is looking for, but there's all these variations on that. And if they're not in the right variation of what that organization is looking for in terms of this abundance of skills that are required for that given position, they're still out of luck. I mean it's really a crapshoot for students today in terms of trying to find the right fit and represent that sell represent their own ability to fit to the organization that's looking to hire. It's. I'm, I'm really glad that I'm not out there in the job market in that way right now. It would be brutal.

Yeah, so I think, I think that's true. It's like if you think about it as this AI wave comes in and this series of automation tasks, basically this AI made certain things simpler. Those are like the types of tasks, like a bulletproof task that you always used to give to the new hire. Basically, you know, it's kind of like the ground work. And for someone as an early Hire recent graduate. Those type of job were kind of like the first step on the ladder. How to for example you write a complex SQL query to get the data, make simple pythons and get your hand dirty with the company's data. You learn about it and also you show your skills. But now it's no longer like that. So you need to basically find the correct fit, what they exactly want, what they want to build. So I show that I can build that. And there was this study from OpenAI and University of Pennsylvania that they look at this task exposure to large language model and the takeaway that they had was pretty simple. Any repeatable tasks that used to be given to juniors. I highly vulnerable to AI basically and innovations. So if a junior analyst used to take all this afternoon write the SQL queries and make the dashboard, now AI can just write it with the great prompt. Right. So basically the economy case for hiring a big group of, you know, terrain and have them to do the work and has ev evaporated. You know, there's kind of like a change. For example, I used to hire lots of interns to basically help with the development and speed up the process. And since AI shift, to be honest, I just to use AI for all of those tasks, you know, so, so this has been this big change. And of course you know we, we are seeing this shift in hiring strategy kind of everywhere. In big tech or even in startups they're just stop hiring for potential and they are starting hiring for proven capabilities. It's kind of like that. The paradigm has changed. New companies these days basically afford to bring in 50 juniors or spend spending a couple of years to train them. You know, they rather to hire five or maybe 10 people that already have built or developed some complete system from day one. So it's kind of like if you think about it, the new entry level jobs is technically what we would call mid level engineers. Couple of years back, you know, shift is really bad. And with this kind of, with this new bar, it's not like that, you know, you don't need knowledge. So all this, you know, deep statistical knowledge, Python skills, they are essential but they are just at this point they are kind of prerequisites. They are the ticket to the game. They are not how to win it. You know, it's kind of it's hashtag, you need to prove that you can build, the company wants what you built and then you know you go for hiring.

I'm wondering because that bar has been raised like you say the kind of mid level positions that we used to call mid level or maybe the entry level ones now how does that change? Because I mean maybe this is a negative view that I'm about to give, but I'm very pro, you know, higher education. But I also think like even whether you look at computer science or data science sort of education, a lot of that does not, even before, before the recent shift that you talk about, it didn't always connect to what you were actually going to do in your day to day work, right? So now not only does it not connect to that entry level kind of day to day work, but does it now even increase that divide where like the, like how, how could we possibly train people to come in as mid level kind of data science folks? Because I think if what you're, if I'm interpreting what you're saying correctly, it's not that AI is making data scientists no longer relevant or AI or machine learning people no longer relevant, it's still very relevant. It's just the stuff that entry level data scientists or machine learning people used to do and kind of level up on that's no longer available. So where are they going to do that? And is it even reasonable for us to think that universities could help get them up to that level?

I guess, yeah, I think so. I would, I would answer to that question in two sections. I think one part is about where is academia stands right now. And then the second part will be talking about the industry versus academia right now. So let's just start with where does academia stands? You know, if you think about it, and I kind of call this, I don't want to be negative as educational bottleneck, you know, and to be clear, first thing is that you know the faculties that we have in CS Data science department, they are all brilliant, you know, they are like world class at teaching the fundamentals, the math theory, history, the research. That that foundation is non negotiable, you need it. But the curriculums often just stop there. And it used to be also kind of like that and it's about the theory and leaves basically this huge gap between what the student learns and what employees actually need for them to do on the first day. As an example, you know, a student might spend the whole semester learning about the math and all sorts of like optimization back rep techniques and stuff like that and which is necessary. But as soon as they graduate they basically see this job market that wants them to deploy on the kubernetes or they know how to work with all different cloud resources, you know, so they know exactly how the Engine works but they actually never tried to drive a car in the traffic. And that, you know, there was this new post by Andrew Angie recently that he argued this urgent shift in education. I'm going to paraphrasing what he said. He said knowledge is great, but skills are greater. Meaning that in the field that's moving this fast, you have to teach the practical skills to get the work done. You know, you need to give the capacity to get meaningful work done by having a proper knowledge and proper training. So this is exactly what the job market is selecting for now. So that's, that's, that's, that's the view that I have on education at the moment. And the second part that we can basically talk about is like a comparison between where is industry versus academia and there is a really good basically study by MIT recent study basically that the stats are staggering. Basically they say that right now about 70% of the AI PhDs are just skipping academia and go to job market, go to basically industry directly. And that's a huge brain drain for the universities, you know, and the second is that which is the real killer risky and probably I'm sure you know what is like 96% of the major state of art systems comes from industry labs, not from universities anymore. So university is already falling behind. And then companies like Google, Meta OpenAI, they are the one that defining the frontier now. They are building the tools, they are setting their standards and that's the absolute core of the bottleneck. Academic curriculums moves on a cycle of years. Getting a new course approved, like updating a textbook, it's slow. By the time university approves one new course to be like let's say for example LLM application course to be added to curriculums, the tools have already changed three times. You know, so the entire framework is really different because you know the tool it took a while and that has happened to me also. Like I developed course and take years to get approval to teach that course. And then you need to go back and update everything that you were planning to teach because you know the industry has changed already.

Well friends, when you're building and shipping AI products at scale, there's one constant complexity. Yes, you're wrangling models, data pipelines, deployment, infrastructure. And then someone says let's turn this into a business. Cue the chaos. That's where Shopify steps in. Whether you're spinning up a storefront for your AI powered app or launching a brand around the tools you built, Shopify is the commerce platform trusted by millions of businesses and 10% of all US E commerce from names like Mattel, Gymshark to founders just like you with literally hundreds of ready to use templates, powerful built in marketing tools and AI that writes product descriptions for you, headlines, even polishes your product photography. Shopify doesn't just get you selling, it makes you look good doing it. And we love it. We use it here at Changelog. Check us out. Merch.changelog.com that's our storefront and it handles the heavy lifting to payments, inventory, returns, shipping, even global logistics. It's like having an ops team built into your stack to help you sell. So if you're ready to sell, you are ready for Shopify. Sign up now for your $1 per month trial and start selling at shopify.com PracticalAI again that is shopify.com PracticalAI.

So Ramin, I love how you highlighted this kind of divide between academia, industry, like what that is in reality. Anecdotally I remember actually last, I think it was last year or maybe a year and a half ago, I lived by Purdue University. I was like walking through campus and they were just finishing their, they had this new building, right. And so this was 20, whatever, 2024. Right. And it said like hall of Data Science. Right. And I thought that, my immediate thought in my mind is like in 2017 you could have created a Hall of Data Science. Now you need a Hall of AI like you, you're building the wrong hall. To their credit, I think they actually, so I just looked this up while we were talking. They did rename it hall of Data Science and AI. So to their credit they at least caught up with the name. But yeah, I guess like obviously you are an educator and so you see that there is value in trying to have these, you know, formal, you know, formal education serves a purpose and is different from maybe on the job training. What do you think? Or have you seen examples where this sort of practical skills are built up in an academic environment rather than just kind of the theory or the, or the knowledge as you were kind of drawing the distinction there.

Yeah, so actually that's something that we have been doing for the almost the last three years. So I basically developed this course, this mlops course at Northeastern University almost three years ago that we have been ongoing. So the idea was this, this is like a, about three, four years ago and I was this hiring manager and I used to do lots of interviews for our team and I always basically interviewed this smart, motivated, you know, good to school, basically candidates. But most of them struggled with the same thing they understood the theory, but they couldn't build anything, they couldn't ship anything, you know. And that's when it clicked for me that okay, if the industry, I personally as someone who was in the industry and academy, expect these students or these basically candidates to build real system from day one. And then I know in the industry we don't teach them that. Could we do something about it? So I started working on this course, I built this M Labs course that every semester right now we have about 150, 270 students within one class, like a huge classroom. And instead of just learning the concept, they start by choosing a domain that they actually care about. Healthcare, finance, sport, robotic, one whatnot. Then as a team they spend the entire semester on building a one real product. And this real product, it's not just homework assignment, it's not a toy example. It's a real working system with deadlines, milestones, deliverable, just like real, like an actual ML and software team. And the best part of that is we wrap up this semester. You know this, the way that we wrap up the semester is that the students basically present their product at our MLOps Expo, which is a full industry partner event we have been holding over the last I think two years now. This year for example, we partnered with Google. So we are Hosting on in two weeks December 12th at Google Main campus in Boston and where our students are pretty hyped to come there, but they would basically what they do, they show the demo, the actual product that they have built. And so the whole course is simple. You don't just learn ML anymore, we teach you how to build with it. You know, and the idea for me was to give the students this hands on experience that companies are looking for right now. And honestly watching the students go from I have never deployed anything before to me and my team, we build a real product this semester. That's kind of like the best part for me.

One at least hypothesis that I have here, which I would love your opinion on Ramin is on one side you have highlighted how this kind of gap is widening even like the between the theory and like where you need to come into a job like at a mid level. At the same time this revolution of gen AI has been happening which in some ways to your point, some of those things are the things that are being automated by AI, but it's also enabling maybe this like younger generation of software engineers, AI people to actually perform at a higher level out of the gate, but in a different way. So not like there's kind of A burden on maybe us as prior generation data scientists and machine learning people to understand that students and new hires need to from the start be doing their data science work differently. So just by way of anecdote, we were talking about this a little bit before the show that you know my wife owns a e commerce business. Black Friday Cyber Monday just happened. I, you know, day to day in my company, you know, I not doing as much kind of hands on work on the product as I was given my, my role as CEO, but it was like it was nice to go back. So for like four days I helped them during the sale and I just sat in a room doing like customer lifetime modeling and like updated forecasts for 2026 and looking at churn and analyzing like customer journey and all this stuff. And number one it was a ton of fun. But I was kind of coming at it from that perspective and kind of reentering some of those things that maybe I hadn't done as much for a little while or even maybe since the previous year when I helped them with forecasting. Like I was able to get tons of that done so quickly because I was having AI honestly write most of the code for me. The thing though was I still had to play the data scientists to get from like point A to point B. There was no way that like I could have just said to any AI system like hey, I want like write a three sentence prompt and get out all of the you know, lifetime modeling and forecasting and all of this stuff. I still had to play that kind of data science orchestrator and know what the things were, know what you know, modeling techniques were relevant, know maybe what trade offs were and other things. So do you think on the one hand it's maybe depressing that the academic kind of industry gap is widening, but on the other hand maybe there's, am I right that there's an opportunity to actually like lean in for, for these students in terms of different ways of their of working to like get to a higher level faster.

I'm not sure about the higher getting to the higher level faster part, but just I, I saw a new talk recently by Neil Hoin over at Google and he made a great point that about this data science job and basically was saying that the data science job is not gone but AI is just forcing them to change dramatically. It's no longer about analyzing the data or building certain sort of dashboards and stuff like that. As we say, you can just video the knowledge, just prompt it properly and just having the data and just build that quickly. So there are certain types of tasks that he used to do for try to climb the ladder to learn more and more but that they are not the same anymore. And the expectation is not for you also to do the same task because you know, if company is hiring you probably these days they want more. But I think it is really great point that for hiring managers or for someone that when you hire someone on your team or have someone new juniors on your team, you need to also account for helping them to like mentoring them properly to be sure that they can evolve and learn. Otherwise we basically take this cognitive ability from them because they everyone if you just ask everyone to just build, build and they just use AI, they don't, they're never going to learn basically how to build. So we take that cognitive ability away from them to just build new faster products.

Yeah, I think you're really onto something there in terms of, you know, like one of the things that I have done for the last few years is I'm a capstone sponsor for capstone projects at Georgia Tech in the College of Computing and I'm doing that from my nonprofit role as opposed to my day job. And so when I work with different teams there, I think one of the challenges is they're kind of bringing what they know. Certainly Genai capabilities have helped them, you know, step up a little bit along the way in terms of figuring it out. I think the areas that I've noticed that they're still struggling, the students are. There's, you know, going back to Dan being a data scientist over the weekend instead of a CEO in that moment is he's bringing all that business knowledge, you know, years and years and years of business knowledge and understanding about what's really needed in that. And I think that's, you know, that's one of those things that is part of the struggle with junior level is there's the kind of concept of I've learned tools in university and I'm trying to bring them to bear and they're not always the right tools for the organization they've joined and they don't necessarily know how to combine that with all the other tie ins that that organization may need that were not necessarily something accommodated in their academic development. And so you know, that's kind of exacerbated by the fact that now with gen kind of replacing a lot of those junior roles coming in and you know, how do you, how do you ramp up? It does seem to your point like things are actually getting like even though we have new amazing tools in, in the form of gen AI capabilities. It seems like things are getting harder to bridge that gap. And I'm not sure how you do that because it's a combination of both kind of the, the experience of being in the real world along with fast moving, you know, a fast moving technical landscape to navigate. Are you seeing that from your side with students and, and how are you tackling some of those, those subtleties that are there?

Yeah, actually, definitely. So I, two weeks ago I sent out a survey to my students and I asked them basically to take a couple of questions and I specifically did it for our talk. And so as part of the survey basically there were some questions and one question was which is 60% basically of the students they say that they are taking online courses on top of what they are taking in the school. In another question, 82% of the students say that they participating in hackathons in order to learn to how to quickly to build. And about 46% of the time they are attending workshops. So they are building their own parallel curriculums through side project open source contributions or certification through AWS Google. And that's exactly it. The portfolio kind of has become a new credential. It's no longer about your grade, it's like about what you have as a portfolio. And this is also important for us to. It's kind of like a dose of reality that this self learning path isn't easy and isn't equitable. You know, it takes tons of time and costs lots of money. And if you want to practice building a real production grade system, working with a cloud service that always cost money, you know, it's those commercials and how many students like if you think about students already paying thousand intuitions, they cannot also afford hundred of dollars per month for cloud computing, you know, to practice. So it's kind of like a huge change. It creates this resource divide and at this point I think the bar isn't just higher, it's kind of also financially more expensive for, for the students to, to learn. And right now, for example shout out to our friends at Google, you know, they give us a lot of credits for our MLOps course every semester because our students, they can't otherwise build anything in the real world. We and I personally reach out to lots of providers in the industry and I say hey, you know what, we train these students to use your tools, give us some cloud credit so they can basically learn and build a phone. But yeah, so that's my take on that.

Well Ramin, I am kind of intrigued because well, on the one side you're thinking very in an innovative way about how to bring this kind of skill or reducing the skill gap, being creative in the academic setting to get people these skills. But also, you know, you're a practicing AI engineer. What have you seen kind of personally because you're already operating at a higher level. Are there also changes any like significant changes that you've noticed in your day to day work over the kind of past few years that have caused you to think about your day to day tasks differently, like more so than the entry level type of folks, but actually ways that like you, you're fundamentally thinking about like your workflows or how you're doing those kind of higher, maybe higher skill or higher level kind of data science, AI stuff. I'm wondering if anything stands out for you.

Yeah, definitely. I mean I personally have been part of this shift. I started my career as a data scientist then like a 20, 2018 I started as an ML engineer and it just went up. Then last year I started as an AI engineer. So I also have been part of this change myself.

Data, ML, AI, same pattern.

And for me when I look at them, they are kind of similar if you put the data science aside because that was kind of like there was no production, there were lots of research especially around it. But when you go to ML and AI just the terminology is different, they're technically kind of similar. The only I think that the main difference that I personally felt is that I need to in my day to day work to work a lot with LLMs because is a requirement for certain things and work a lot with the larger models which requires you to have a better understanding on you know, kind of like a GPU optimization, how to break, break your models and basically ensure that they're optimal basically. And those changes, you know, it wasn't something that you do maybe a couple of years ago. So I ended up personally trying to read a lot, you know, spend summer time, just read different books to learn to advance my own career. And I always talk about this with my students. When I learn something new, I bring it to the class. I was like, okay, I was recently basically reading about this and this was really interesting. This is the link and maybe I sometimes give them a small lecture also on it. But I think yeah, so it's like the change is there for everyone, not just for a junior. It's like it doesn't matter if you're a principal or a junior technically, but who's getting being more impacted. I think that's the part that's kind of like unfair, you know, to the juniors or recent graduates.

I'm curious to extend this out a little bit. You know, as we kind of went from the challenge of juniors and Dan introduced, you know, the challenge of kind of us, you know, as people who are past that point in their life. But like we have fast coming, you know, fast changes are coming even more in the sense of like we're hitting that point where physical AI is really on the rise now, you know, not just in certain industries as it has been historically, but in many industries. It's exp. You know, it's exploding outward at this point. And we all have challenges in terms of incorporating these new realities into what we're doing and how we're going to learn about it. What does that imply at the university level when you're getting back to students and they're already, you know, you're already trying to bridge the gap into the corporate world or the startup world or wherever they're going to be productive. But you also have this explosion in terms of the places that AI is touching in new and different ways. What are the implications on the curriculum and on the burden that professors have to try to get their students ready for that next thing, which is steamrolling.

Over us already, I think it depends. So let me just. I know some other schools are doing that, but I'm going to speak with respect to Northeast since for example, New Artist and Curry College of Computer Science. The as of this year, basically 2026, they're updating their curriculums finally. So they, and not everything is going to be a small shift, but gradual basically. So they are introducing some more practical, practical courses into the curriculums. And they also, for example, they're weaving the ethics directly into the coding part of the curriculum. So, you know, but this is going to be kind of like a slower shift on the curriculum side but on the other end from the teaching perspective, you know, and this is like kind of like AI is kind of like a double edged sword at this point because students, you know, they all use AI, they are using degenerative AI. So they basically, which is great, I would say my students, you know, use it, but don't lose to it. You know, kind of like you need to use it, don't lose it. So it's kind of like you need to be sure that you can learn, move faster with this type of thing, not to just give away all the autonomy and you just, basically you just use them for everything. And so, and then further from the other end from the teacher's perspective it's, it's kind of difficult because when you give for example homeworks or labs to students, it's just especially coding. I'm not talking about writing an essay like coding perspective. You don't know, you can't even tell that if they wrote the code or not. Everyone returned great codes these days. And then there's a homework and there's no way for you to just say that if it's written by AI or not, they're really smart to how to change the temperature to ensure that the result is not being detected. So again this is kind of like a double edged sword but also from the other end is like because there are lots of information, lots of changes in the market, in the industry, in the domain at. Every day, like every day you read the news, there's a new article, there's something coming out and it's hard for basically academia to keep up with that. You know, it's like are you. Academy is falling far behind the industry and it's going to go into this, this gap is going to just expand the way that it is. And I think at some point industry need to help academia. It shouldn't be just academy need to keep up with the industry. If the industry needs new talent to come later, you need to step forward and say that okay, you know, let me also help them, let me start some program, let me participate in some of the courses that they have, you know, so otherwise it's kind of like a chasing a ball, like a academy just constantly trying to keep up and that's not going to win.

That's fair and I think that's a, it's a good notion that I think industry really needs to consider is in investments back. I, I agree. I think it's been largely a one way street there. I, I would like to flip a little bit the timeline around to, to the students that are coming in. So, and I'm asking this selfishly. I have a 13 year old daughter in eighth grade. She is, we're, we have been applying to, to magnet schools and things like that and getting her ready for her high school experience. And she has never been someone interested in AI. That was dad's thing and all that. But as she has started looking at what she wants to do, she's starting to recognize that whatever that is, AI will impact her in a significant way going forward. So it's not just the kids that are focused on technology at this point, but all of the kids. And as she does that and they're entering into high school school. What advice do you have for what high schools need to do before they come to you, before you're getting those students and you're trying to prepare them for industry and a career and moving through their lives, you have students coming to you. What would you like to see from high schools in terms of how they prepare these kids to be better or more ready to come into your care as a professor so that you can do the thing that you do?

Yeah, so I think, I think that's a great point. And there are already two shifts, I have been spoken like by neighbors similar question that, hey, my kids, should they go back, go to college for computer science anymore? Should they study this anymore? And I think the answer is that yes, you know, there will be shifts in the market. And it's not just computer science, it's not just AI. AI is going to impact so many things. Some, some areas like a slower, but some areas much faster. And at some point all of us basically become somehow we need to learn how to work with AI. And I think it's really good if from high school you understand the concept, not, not maybe the master theory behind AI, but just to learn. Okay, in general, what, how does AI work? There are lots of AI capabilities that you don't technically need a math behind them. You can just build a system just by knowing how to put the components together. So if they could like from high school go to part of the workshops or participate in some sort of like a training that build something simple, you know, that automatically opens lots of door, like a thinking process for you for the future as you go through like a, after high school and you want to go basically to universities and you learn in different courses, different concepts, you're like, oh, I know, maybe I can build something around it. This, you know, I always think that everyone can be an entrepreneur. You know, it's kind of like as long as they have the correct mindset and the energy for it. So if, if they already have been trained from high school and they have not, not trained again in a bigger way, just kind of this easier way of training like teaching, they could potentially advance more in university in compared to students that they just want to learn during the university.

Well, I know that we've talked a lot about kind of a lot of perspectives, both from the industry side, from the academic side. I think all of us on the call though are generally excited about kind of certain parts of the ecosystem, the way that they're developing from that side of things. As we get closer to the end here, Ramin, what. As you look at the ecosystem, because you're again, you're, you have multiple views of this ecosystem from the industry side, from the academic side. What. What's most exciting for you as you're kind of entering into this next year and maybe it's something like, oh, I can't wait personally to, you know, have the time on a weekend to explore this. Or maybe it's something you're, you're already getting into.

But yeah, definitely. Actually I recently purchased the Richie Mini by.

Yeah, yeah, yeah, the robot. Right. The little. It's kind of a desktop type robot.

Yeah. So it's. I'm so I'm pretty excited and waiting for that to be delivered. I think is the delivery is going to be early January, hopefully. Finger crossed. And I'm pretty excited to work with that and build some capabilities I have in mind. And when I think about all these changes, like if you would put me back a couple of years ago, I would have never gone for robotic. Oh my God. Now you know what, it's not my tank but now with this AI change and I already went through the contents of unhocking face, which is. These guys are great. But reading it through the documentation I was like, wow, that's pretty straightforward. So think about how much AI or change the field that I can easily go buy a robot, like a small robot. And I'm planning already ahead of time. You also have this simulator so you don't need to wait for it to deliver. You built ahead of time the apps and simulated that it will work on the robot and when the robot comes, you deploy it to it. So that's my go to what I'm excited for in 2026.

Yeah, it's kind of crazy. I feel like when we started in this field it was like hard enough to get the dependencies installed for TensorFlow and just be able to run any model just like that in and of itself was like, are you trying to give us PTSD?

Is that the goal here? I mean TensorFlow and CUDA. Oh, yes.

Yeah. It's like regardless, that was the hardest problem and now you can have a whole digital twin of a robot and do all that. It is pretty spectacular.

Yeah.

Well, I'm also excited for that. I think we do have one coming here to our offices as well. So I'm excited to see what that's like. I've never done any robotics really other than maybe those like what are those LEGO robotics sort of things. But, but yeah, excited to excited to see where things are going. Thanks for sharing some of your insights with us, Ramin. It's been, it's been a real pleasure and hope to have you on the show. Third time to let us know how the robotics went.

Yeah, appreciate that. Thanks for having me again and it was great.

Alright, that's our show for this week. If you haven't checked out our website, head to PracticalAI FM and be sure to connect with us on LinkedIn X or BlueSky. You'll see us posting insights related to the latest AI developments and we would love for you to join the conversation. Thanks to our partner, Prediction Guard for providing operational support for the show. Check them out@prictionsguard.com also thanks to Breakmaster Cylinder for the Beats and to you for listening. That's all for now, but you'll hear from us again next week.