AI, Advising, and the Future of Student Success - Adventures in Advising

In this episode of Adventures in Advising, Matt talks with Arjun Arora, founder of Advisor AI, about where artificial intelligence fits into the future of academic advising, and where it absolutely does not. From chatbots and career pathways to implementation headaches, ethical guardrails, and the myth that AI can replace advisors, this conversation dives into the real people, processes, and possibilities behind emerging technology in higher education.

Arjun shares how Advisor AI is working to support students from enrollment through graduation while keeping advisors, career professionals, faculty, and student success teams at the heart of the journey. Along the way, Matt and Arjun unpack what institutions should ask vendors, how campuses can measure success beyond “we launched it,” and why the human side of advising remains the secret ingredient no algorithm can bottle.

Grab your metaphorical compass, because this episode explores AI, advising, and the winding road toward more intentional student support. 🤖🧭 

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Transcript

Matt Markin  
Hey, what's up. This is Matt Markin, and welcome to the Adventures in Advising podcast. You know, Ryan and I have had various episodes with guests discussing AI and how that relates to and impacts higher ed, especially within the academic advising realm. And this episode continues that conversation. And let me bring on our guest for today, and that is the founder of Advisor AI, and that is Arjun Arora. Arjun, how are you?

Arjun Arora  
I'm doing really well, man. Thank you so much for having me on today.

Matt Markin  
Yeah, absolutely. And I know Advisor AI's mission, according to the website, is simple: to empower every learner with actionable pathways to achieve their boldest dreams, while ensuring educators remain central and serve as a catalyst for intentional transformation, and this to me is an ongoing conversation discussion within higher ed. So, really excited for this, and as we were mentioning before we started recording, I was letting you know that I almost didn't know we were going to have this, because we had where I'm at, the fire alarm went off about 25 minutes ago, and I was getting prepped for this interview, and then I was just waiting, like, okay, when are we going to get the all clear to get back in, and then once we got the thumbs up, I'm trying to weave through everyone to run, run back upstairs just in time for this, because this is a conversation that needs to be had, so I always go by the quote, if you want to make God laugh, tell him your plans.

Arjun Arora  
I feel like the fire alarm aspect is always back of mind when we have webinars or events that are virtual, like what do you tell 100 people, right,

Matt Markin  
but it won't be adventure than advising unless I ask you this first question. That tells me your origin story.

Arjun Arora  
For sure. The last, like, 10 years I've spent on AI and data and technology implementations, really. So that's been the core part of my background, you know, ranging from technology companies, manufacturing companies, and the last four or five years really focused on higher education. I think what's really the motivation behind Advisor AI, and how my experiences informed it, is twofold. Firstly, I did not see much social impact often, so you know there's really good chatbots or really good technology, but Who is it helping? And I always found a big disconnect there, which is there wasn't a clear impact associated with things, and on the other side I also saw that the right advising support a milestone driven approach a much more life-designed strategic way to build skills helped me move from someone who was maybe 1520 years ago was afraid to ask questions to leading projects that serve millions of users and moved into real opportunities that I thought I was initially not exposed to or thought I could get into, and so I think it's driven by this dual nature of how can technology help people, but then how can also the right infrastructure, the right high quality support be made more accessible.

Matt Markin  
Gotcha, and I think let's, let's maybe take that technology part of it, you know, because in academic advising, let's say it's oftentimes kind of maybe been undervalued relative to the work and efforts that advising departments and advising teams put in at their institutions. Do you, in your opinion, do you believe technology can meaningfully address that type of concern?

Arjun Arora  
Yeah, I think a background or context here is we have really spent close to two, three years doing research with advising teams, so I've had the chance to probably meet advisors from more than 200 different colleges, we have met, you know, academic advisors, career advisors, student success leaders, administrators, as well as also students, right. So that has also been part of what has informed what we do, and I think one of the bigger insights there you're spot on with is I think there's so much work and effort that advisors are putting in managing caseloads of like 1000 students, but it's not recognized accordingly, and I think technology partly is to blame here, which is it's so complex, where so much time is spent on maybe data entry and setting up systems that. That time is not spent as much on the high impact or student oriented tasks, and I think technology can also fix it from the lens of like if a lot of the administrative back and operational items can be made simpler, less busy, it can help not just the advisors be more effective, impactful in their connection with collaborations, but it can also give insight to leadership about how the role, how the profession, how the interactions are informing institutional outcomes for enrollment, graduation, workforce readiness,

Matt Markin  
and I'm glad that you mentioned that, like you've talked to so many different individuals from all various levels, including students, because you definitely want to get their opinion on stuff, but as, but not just even just administrators, but those that are actually on the front lines doing the actual work, so I really appreciate that you've done that. Now, speaking of, like, let's say AI, you know, this is a depend on who you ask. Some are like, I don't want to use it. Some are like, let's use it, no matter what. It's here, it's been here, and you know, I think some people don't realize that how long it's been around, and then also, like, where it's actually being used within higher education at institutions already. So, I guess my question to you would be, How do you, how do you think AI is already being used within higher ed that others may not know about?

Arjun Arora  
Yeah, I think I always like to step back on this question, which is, I think, what I'm seeing right now a lot of is here's AI, here's technology, let's go find a problem or an area to apply it to, which is not the most effective approach to get results from any tool, and based on, like, the last 10 years of me implementing a lot of similar solutions, those projects often get blocked or don't move forward. I think a better lens or approach here really is to start with what are the areas teams are challenged with or looking to improve. Right, is it enrollment, is it graduation outcomes, is it integrating more workforce readiness into the curriculum, and then work backwards in a way from what are those use cases for AI, and I think if you take that approach, there will be so many scenarios, right, that AI can be applied to. I think, right now we definitely see AI can maybe write a resume or write content for a resume, build a career plan, build an academic plan, create a checklist. I think those are now fairly common, widely used. Most teams are aware about how just most technology or tools or chatbots can help support that, I think. What's maybe not as visible, and what we are really seeing with partnerships right now is using AI in sort of this lens of an invisible infrastructure, so it's sort of like it's something that's happening in the background when it comes to maybe optimizing a caseload, right, or creating a much more integrated holistic plan that maps academic coursework, that maps career readiness milestones, that maps maybe clubs or communities Arjun should join in engineering, and so it's much more of this operational aspect that can streamline a lot of the effort students are putting in and advisors are putting in.

Matt Markin  
Yeah, I mean all great ideas initially, but it's always that implementation part where you know you might have, let's say, just within an academic advisor or career advisor, where it's like, okay, here's this new thing, now go ahead and use it, and they're like, I don't even know how to, I'm even trained to audit. How do you go about helping, like, an institution or a department understand, but also be able to implement as well?

Arjun Arora  
Yeah, I think what I hear very often, almost every week, right now, is technology is so complex that it requires six or seven weeks to train, to enter data, to show students what to use, how to use it, and then even after that, students don't engage, right, and that's partly technology, and how complex it is, and it should not be made that complex, right? It should be easy in order to, like, figure out what to do. It should be intuitive, I think. So that's a perhaps a blocker that I've probably heard way too much, I think, because technology. Should be made extremely simple is always the recommendation. There,

Matt Markin  
yeah, I think kind of going along with that for this next question is like, you know, I think you know when I go to conferences or talk with colleagues, you know, many of us kind of worry that when there is a new product, or like, let's say, an AI vendor, will they misunderstand what academic advising is, and the, you know, and do they see it more as information delivery rather than an actual relational practice? How would you respond to that concern?

Arjun Arora  
Well, I think, so I have done a lot of self-study on advising over the last 10 years, most of our team is also academic and career advisors, right? And I think as advising professionals, or someone who understands the space deeply, we know that technology is only a part of advising or can support it, and so my recommendation there is always to push back, right, which is to ask vendors, like, how do you meet the basic minimum requirements for advising, right, like for career exploration, for holistic advising, for guided pathways, right, how are you actually implementing those best practices, and what are the methodologies being used at each step, so that it makes the vendors also accountable, right, about what they need to be doing, versus saying here's a chatbot, it can do advising, right, which is so far from the truth, like a chatbot can, like, help you answer questions, it can create those plans, but that's also not what advising is, because I'll just look back at maybe 15 years, or 15 years, or so, 20 years. I remember my advisors from school, right, and I think I remember those interactions, experiences, the encouragement that helped me take that next step. I don't remember what laptop I used, or like, whether they had a chat bot or not, right. And I think we always be with the with the trends happening right now. I think we missed the context that advising is much more than just creating a course plan.

Matt Markin  
Yeah, definitely has that human element to it, but I definitely agree with you, and like, when you think back to college years, or some sort of experience, you lot of times remember that person, that interaction, good or bad, but it is a memory because of that interaction that you had, versus yeah, what was the technology that was used for it, I'm sure you get this question too, where it's like, you know, and I guess speaking of the human element, is well, is AI going to take away my job, you know, and and I think it's also kind of understanding, yeah, that academic advising is more than just let's plan out your courses, there's so much more involved in it, but do you still kind of get that question from individuals or their concern that this is going to take away their job or the less amount of advisors that will be hired.

Arjun Arora  
Oh, yes, we would. That is a very common question. I think the question is also perhaps somewhat driven by what we're seeing in not just in higher education, but in other industries, right, where technology can easily be seen as a substitute, but I think it sort of is missing the context of like what advising and education is really, right, that there is an aspect of building connections, engagement, providing care, providing support, providing encouragement, which are not areas that technology should be involved with, right? So it's important to have that like boundary, really. I think where what are the areas technology is great at and get help support with planning organization, but at the same time drawing that boundary, and I think when we, I always take this approach, and of like providing education on the topic, right, which is I think that's what anyone having that conversation on campus, right, is should have the vocabulary or understanding of helping easily communicate that these are areas that technology is great at, can really help us move that, move things to that next step, but I think on the other side these are areas that humans or people have to be integrated, right, because if Arjun, for example, is debating between three different career paths and is anxious and not sure a chatbot can prompt you as much, but that's not something that's going to get solved just through a chatbot, right? Does require a discussion with an advisor or someone who can really help the student. Wouldn't figure out the nuances of, like, what's holding them back, also from that option, right? Because I think I hear this often, where even if I look back at my experiences on this topic, like, my family or peers recommended me to maybe move into a finance major, because of it, good at mathematics, but within that space there are like hundreds of occupations, right? I think until you don't have exposure to those, you're not going to take that chance, because maybe someone in your network has not done that right. Advising can really help someone make that decision? It's not so much about your background, it's much more about like where you want to go, and like, do you have the skills to actually get there.

Matt Markin  
Absolutely, I mean, based on what you just said, is there anything else that you think higher ed professionals, maybe in general, misunderstand about AI? 

Arjun Arora  
I think there's like two bigger themes there. So, the first theme is that AI can do the job of advisors, right? That's sort of, I look at a myth, really, which is I think we should not start with that lens, and I think teams that are not doing advising, we hear that question a lot, that advising can be automated by a chatbot, right? So some education there would be helpful, I think. The second part of that is much more from an implementation standpoint, that you also brought up earlier, which is technology is often so complex, fragmented, so is that the expectation to have, right, and I think teams should really challenge vendors, right, about like things should be made simple, like it should not take three to five months to launch something, as well as I think what I'm seeing right now on this specific topic, right, is advisors are often building their chat bots and launching it, or sharing it with students, or with other campus partners. I think being in sort of the AI space, like, there is much more nuances and consequences to that, right, and if I really double click into a couple of things that teams should be made, should be aware about is that building a chatbot that can answer common questions, great, but then you also have to think about who is responsible for managing this for the next six to nine months, are you going to be updating it, I don't know if advisors have that time. I think, additionally, like, if student information or certain sensitive questions are added, is this chatbot or system going to expose it to a more public domain? Right, and I think we're seeing a lot of lawsuits and regulations around this topic that the general purpose AI systems have access to these to this information, and will not ask before using it. Also, right, I think there's the need to have a much more restricted way to do these tasks, support these workflows, and then lastly, sort of maybe similar part of this implementation or technology aspect. I think we also need to sort of be better, I see, at connecting the dots of this is an institutional priority, and an initiative, and a siloed chatbot will not be seen by often administration or leadership as a key requirement. Right, it has to be something that's organizational, which requires a different approach to scaling this type of support or tool.

Matt Markin  
Yeah, totally agree with that. So, I'm interested, really, also about if you could tell me more about you being the founder of Advisor AI. Tell me more about Advisor AI. What's the history behind this?

Arjun Arora  
For sure. So, so my background, right? Like, I think the AI technology experiences, as well as the impact of advising, is really what motivated me to move in this direction three or four years ago. And it's interesting when someone asked me, like, what is advisory, and what we do now? Because four years ago we started with a very simple major exploration assessment, so business student could like answer a couple of questions, they would get recommendations of, okay, you could take finance map certain labor market data, marketing. Think, and then you go sort of discuss those results with an advisor, very different to what we do now, and I think a lot of that to where I'm heading with what we do now is informed by what me and the team have learned in the last four years, which is, you know, I think students have cited it's not difficult to navigate all the resources that are on the website, the catalog, the career pages. Advisors have also often shared their having to manage so many different systems resources. It's often not saving time to focus on the tasks that are critical, that are part of high-quality advising, and so based on those conversations, experiments, what we are now is it is really a AI native student success and advising platform, and so the three pillars to look at it would be it is something students can use from when they're pre-enrollment, so exploring options to decide, is this the right program major for me, and then once they're enrolled, really a system that can help them be accountable, moving through each term of academic planning, career resources, institutional resources, sometimes I may not even know, like a campus resource or a social media certificate was available, so providing that early exposure, while at the same time academic advisors, career services, student success, faculty are integrated into the process, right, because student also doesn't know who to meet with and when to meet with a career advisor, and they are again a critical part of what high-quality support infrastructure looks like. So it is really helping not just the student navigate options, but also helping the teams be part of that process at every step in a just highly efficient manner, right through chatbots and case management tools and other approaches.

Matt Markin  
I love that. Is there anything that you can say in terms of like what makes advisor AI maybe different from other AI platforms that might be being used, let's just say within higher ed?

Arjun Arora  
For sure. So I think we're, we're seeing campuses from not just the US, but even from countries in Australia and Middle East, and if Europe also reaching out, and what's often emerging is probably in like four or five key themes, so the first area is really the evidence-based approach. So we have integrated the system and implemented the system using a lot of the best practices from Appreciative Advising from National Social Security on colleges and employers, from the feedback we have gotten from like 200 plus campuses in the last sort of three four years, right. About what is high-quality advising, career exploration, milestone-driven pathways, having more secure, sustainable infrastructure, case management support, intervention tools. Right, so what are those things that work, but in a much more unified manner. It's often fragmented, I think. The second part is, and partly I think your question from earlier around implementation, it should not take six or nine months to launch and train teams like our processes. That it's fairly simple, teams take about 10 or 15 hours at most. They launch within the first month. We have advisors train cross training different departments where we are not involved at all, because it's so simple to use. So advising team is just sharing it with the career services team. I think what I've heard is that's pretty rare, because typically it requires multiple Zoom calls or multiple workshops. This is a 30 minute training session. Here's how to do the tasks, and then teams can sort of build off of it. I think the third part, again, ethical responsible AI is not just I see something we see on a webinar, but a foundation, right? It's sort of built in from day one, which is, if I ask questions about mental health, the system will just not respond to it. You will route it to, like, an academic advisor, or like any resource available, because this is meant to help with the academic career exploration aspects, really, and not give advice on the critical nature of advising, right? So we really understand the boundary, which actually helps know lower the level of risk, as well as integrate the right people across the process. I think,

Matt Markin  
Yeah, I'm just kind of thinking, like, from my own experience, and what others might end up saying, where they might say, well, I'll believe it when I see it, because sometimes with the like implementation of, like, here's this new product, now we're going to train you, now go train everybody else, and then that takes like, like you, like you were saying, sometimes these things go like six nine months, if not longer, but in my experience, what ended up happening with a certain property, I won't name what it is, but when we got trained on it, and then it was like, okay, Matt, you and these four other individuals will now be responsible for training other departments, we're gonna peace out. Let us know if you have questions. And then once it went live, like there was all these bugs, and we were trying, we had to put tickets in to get things resolved. And then that ended up taking weeks. And then the people we were training were like, why are we going to use this product now? Because you guys can even get it right the first time.

Arjun Arora  
I think at this point I've heard that same sentiments at least 100 times from different campuses about their experiences, and I think that is really what informed, like, where we have focused a lot, right? So the implementation should be made easy, which is it should be done by it is done by our team, right? So, just for that context, right, where advising teams should not be like entering data mapping resources, right? They understand the domain and they understand the context of the institution, and so they should be directing the resources, as well as pointing out what is accurate or not, and we really partner with teams from that lens of, like, what does success actually mean for you upfront, so that six months out, right, you're not like, well, we launched a system, we still don't know what the use cases are, what it's going to help with, that just never works out right for the for the for the advising teams, for the students, for the administrators, and so I think sort of goes to my experience of is of like backwards design approach and how this system is also technically built with that foundation of if students have a plan upfront, if advisors have the data and resources upfront, if the implementation process is done up front, right, a lot of things can be made simple and streamlined.

Matt Markin  
Now you were mentioning kind of like within the system, like, like it's if a student, if it's like a mental health concern question, it would kind of get rerouted, I guess, going along with kind of around that, a lot of with academic advising can be highly local policy heavy, what I would consider like a lot of gray area that that we work in, so a lot of you know exceptions that that could be made. How would you help, or how should others that might be at a campus take that into account when they think of implementing something with AI?

Arjun Arora  
Yeah, so I think it depends on the AI system, right? Like, what it actually is doing. I can share from our context, which is when we partner with, you know, community colleges, right now. So we're working with their teams on, like, what are those local policies, what are those frequently asked questions that you could maybe save some time on, right? But then also understanding from them, like what are the areas that you don't want a system to like answering, or you want to give a disclaimer upfront. Example for that would be like if a topic we have like high school students going into community colleges, that is one cohort, really right, where high school students may not be eligible for certain types of scholarships, right. And so we need to give a disclaimer upfront. If the student starts asking about financial aid, how can a response be suggested about you are not eligible, even though the resources do exist on the website, you do need some sort of direction or disclaimer there, right? And part of that implementation can be set up in a way that this local policy or department level knowledge is embedded into the system, so that when Arjun asks about financial aid as a high school student, I get the right answer, and I can go explore it further, and if I am enrolled and I asked about a certain topic, I get the right answer,

Matt Markin  
And you were mentioning, like, with let's say success, that you're at. Asking these teams like upfront, what that would look like. How do you help these teams kind of look at success in the terms of like the first 90 days or six months a year, like a year after that?

Arjun Arora  
Yeah, like, so I sort of again start off with, who are the teams you're working with, so you know, is it admissions from an enrollment standpoint? Is it academic advising from just building more consistent experiences, student graduation, retention outcomes, or is it career services teams from the more workforce readiness, integrating career planning into curriculum, like what are the main objectives and who are the teams, and then map out that six 912 month roadmap. So, example, there would be that typically a milestone for the first four to six weeks is really to get the system set up with the core information that's required about advising common topics, career resources, so that by the 90 day term at least an experiment can be conducted and some initial feedback and data can be captured, and so with one of the, one of the colleges, like we did, like a workshop on career exploration, had a couple 100 students engage, ask questions, and now the team had data about that this workshop was effective, and so they can now expand it to other groups and cohorts in the next three month term, and so the six month timeline, or I would say, like a milestone, would be that you have a repeatable foundation, a case study that can be shared with other departments, so that you're sort of adding up, really, which is you're not building up the foundations again, you're finding the insight from case study one by month three and six, then you are replicating it with multiple departments by that month nine, and ideally, like, if that process is taken and the teams, the people, the process, and the technology are aligning right, that's sort of the three lenses I look at, look at things to sort of make it successful by month. Well, this could expand to that every department across the institution is embedding the holistic advising approach, right, where every student has a clear plan, not just academically, but from a career lens. All the resources are mapped, advisors have all the right information, and I think that's what success is, which is a gradual, intentional build up to scaling high-quality support.

Matt Markin  
Yeah, no, I definitely appreciate that answer, and the fact that there would be benchmarks in there, because I think sometimes some departments think of success as, like, well, we implement it, check off the list, move on to something else.

Arjun Arora  
Yeah, I think again we sort of take a very collaborative and meaningful approach, which is we don't want just a system to also be implemented, right. That's not a measure of success, like if it's not helping the student, if it's not helping the advisor, then I think it requires a conversation around like what needs to be addressed, right, because I think especially with the shifts in education right now, where there is significant accountability standards. Standards are also evolving rapidly about what needs to get prioritized and what needs to get deprioritized. I think taking a strategic approach to any project, right, any implementation is highly recommended, because six months out of these projects will be questioned, and they are getting questioned. I think at least we see this almost every week, right now, like every current solution Teams has is getting questioned.

Matt Markin  
Which I think is good. You always want to be a question and reflecting back on it, which actually, with speaking of reflection, you know you were talking about from when Advisor AI started to now, you know, you made some pivots, you made some changes. Have you learned anything about yourself through this whole time with Advisor AI through all this through these years.

Arjun Arora  
Well, I think the maybe the biggest lesson I would say in the last four or five years is much more like going in with this lens of that technology can help address. So I think you know the data and reports you're seeing from the student lens and high stress and also like insights from advisors that we've captured that you know technology could help address 80% of these challenges, right, and I think what my thinking has sort of shifted to, and a bigger realization is that technology is really like 20 30% of the challenge, right? I think the other two components there that I previously mentioned are the people and the process, right. So, from a people perspective, like how our advisors appreciated and have the right resources and have the right training and also recognize for what they're doing from the level of impact to the institution that has to have more better infrastructure right, especially now I sort of look at if technology is making teams more productive, more effective. There should be more appreciation also from the institutional lens. There, and then third piece, there is the process, right? Like people can be highly skilled, technology can be effective and modern, but you also need to have very clear processes in place, which is when should a student engage or engage in career exploration? When should they create a holistic plan? Right, just sort of like if we just have a solution available on a website, no one's just going to actively sort of engage with those systems or resources, right. It has to be made a process, because I think advising is not an optional exercise, right. I think it should be. It is critical to someone's success, personally, professionally, and it should really be treated as that I think some schools have it optional, some have it mandatory, and so I think that's a big insight, probably for myself, and just how things are shifting right from the conversation, there's people, process, and technology, I think all three play a critical role in success of any initiative.

Matt Markin  
Perfectly said. Now, by the time someone's listening to this, hopefully it's when it is published towards the end of May. You have some webinars coming up, right?

Arjun Arora  
Yeah, I would love to briefly share a mention of that. So, again, over the last three years, from our conversations with campuses nationwide, also globally, from what teams are sharing, from advising, admissions, career services, through multiple partnerships that we've built up over the last couple of years, we have curated a series of topics to help share what are the minimum requirements for different departments in driving success, while also demystifying the myths around technology, and so there's sort of four lenses to this. We've completed a couple of the admissions related webinars, right now we're going through a lot of the advising and career services related topics. So, what is the high quality career ecosystem? How should teams embed advising or AI driven advising systems from enrollment to graduation? And then the last part of sessions close to June, sort of the timeframe will be around a lot of the topics actually that we have discussed today, which is what are the stages of a successful implementation, what are the metrics, how should teams really evaluate a system success upfront, right, like what are those key questions to ask, so that six months out or 12 months out, themes are not at the point of, well, we checked it off right, and I don't know who's using it. So those are some of the topics, and they're weekly on Thursdays. The link to register is also on our homepage of the website, if anyone is interested, when they do listen to this

Matt Markin  
awesome, and you know, this is an academic advising podcast. We have many higher ed professionals, from advisors, peers, administrators, various roles. If you wanted, if you could sum up this conversation that we had today. What would you want to tell listeners?

Arjun Arora  
So, I think my biggest lesson from this conversation, and the conversations I feel I'm having almost every week, is twofold. Firstly, with any AI system, any conversation. And really try to map out those three aspects of people, process, and technology. I think setting up that foundation will drive much better success on the student success objectives most institutions have, and how teams are also integrated at every step. I think, secondly, it sort of goes with this lens of like do less, which is I think what I'm hearing often is teams are trying to do so many things, so many initiatives, but there are four or five key best practices that have been proven over the last, like, decades, right, about what works, and so, given just operational constraints, and this conversation, it's really about focusing on what drives excellence. What are those four, I, four or five activities that contribute significantly to student success, and don't try to do all the other 80%

Matt Markin  
And last question, we asked guests this on many of our episodes, you know, with everything that you have going on, everything on your plate, all the conversations you're having. What do you do for your wellness?

Arjun Arora  
That's a good question. I think I'm a big proponent of taking walks, so that's my, that's my like recommendation there. I think we're in an interesting stage of transitions in education and advising, so I think I'm always someone who thinks things can be done better, right? So I'm sure I can find a way, but I think wellness approach partially here is also to just be more accepting of the situations, I think that's like partly what I learned. 

Matt Markin  
Well, great way to end it, Arjun. This was a fantastic conversation, a very meaningful, as always. Thank you so much for being on the podcast today.

Arjun Arora  
Thank you so much, Matt, for having me on your podcast. Really enjoyed the conversation, and I've seen a lot of great engaging conversations previously, so glad to be part of the series.