Arjun Saksena, the Founder/CEO of Humanic speaks at Johns Hopkins Carey Business School. He discusses on the importance of understanding the problem and target users when building tech products and the need to address real problems and avoid overbuilding without focusing on learning and sales. He introduces the concept of personas, identifying user types not targeted by a product, as an opportunity to acquire or build new features.
Graeme Warren: So, greetings, everyone. My name is Graeme Warren. I'm delighted to welcome Arjun Saksena as our guest speaker at Carey Business School today. Arjun is the founder and CEO at Humanic. Humanic is revolutionizing the CRM space to make it easier than ever for growing companies to maximize revenue from their existing users. Humanic is the industry's first PLG CRM for today's modern revenue teams. It boasts industry veterans such as DoorDash, Notion, and Miro as clients. Arjun previously worked on building a platform to solve growth-related challenges at Adobe Creative Cloud and Evernote. He was an early member of two successful startups: Fast Forward Networks, acquired for $1.3 billion by Inktomi, and Stream Logics, developed by Thomson Reuters. Arjun, it's a real privilege to have you here. Thank you so much. We look forward to your talk.
Arjun Saksena: Cool, thank you very much. And as many people as I can be on video, that will be fantastic. So, some of you are studying in the AI/ML space and applying that to business. Would that be a fair assessment, Graeme?
Graeme Warren: Yes. Cool.
Arjun Saksena: So, by a show of hands, how many of you think the generative AI stuff is all hype and will not change anything? And how many believe that it's going to change the game entirely? So let's start with the hype first. Okay, how many?
Speaker 2: Not a single hand. One person. Okay, one person. Okay. Yeah, go ahead. So a couple of people think it's hype.
Arjun Saksena: Okay, so this debate will be settled in the next year, a couple of years, on how impactful it is. What I wanted to talk about, based on my experience, is the difference in approaches between building a couple of startups. I'm currently running HumanIK, but prior to that, I had another startup called Growth Simple. The difference in approaches between Growth Simple and HumanIK is night and day, and that's what I wanted to highlight in this talk. Feel free to interrupt me anytime by raising your hand if you have questions. Because, you know, this is for you to try and understand what you want to do in your career, utilizing all of the stuff that is happening these days, and how you can use your interests, background, and education to be best prepared for the end of the decade to come. Okay, any questions before I start?
Speaker 2: I don't see any hands here. Cool.
Arjun Saksena: So it's good. I'm calling this the new age of AI. AI has been around for many years, and for the past 10 years, people have been dabbling and trying to build businesses. But now, with advancements like Chat GPT, everything is coming together. The next five to 10 years will undoubtedly utilize these advances in technology. As Graeme and I mentioned earlier, I'm Arjun Saksena, the founder and CEO of HumanIK. I have held product roles at Adobe, Yahoo, and Evernote. I'm happy to connect on LinkedIn and share more about my background.
To begin with, let's provide a high-level summary. Most of us agree that generative AI has defined the last decade, focusing on classification and definition. Now, we are shifting towards interaction. I'm sure many of you have been reading about these developments. Here's a quote from the co-founder of DeepMind, who recently left Google to start his venture.
There are broadly two ways to enter this space regarding the generative AI landscape. The first is what I call research focus. It involves understanding why GPT is so great, how it is gentle and flexible, and what makes it so powerful. This area is of great interest and requires significant work. The other approach is the applied machine learning part. Despite the intense interest and questions about the capabilities of large language models, progress has been fairly limited. While there is excitement, the models currently available are more like toy models.
Now, let's have a show of hands to see how many of you are interested in the research side of things. Those who raise their hands can be considered as more research-focused or algorithmic-focused individuals. The rest of you are likely more interested in the applied machine learning and business side of things.
Speaker 2: We're mostly applied here, Arjun.
Arjun Saksena: Okay, sounds good.
Arjun Saksena: That's even better. On the applied side, I want to share a framework with you. Let's look at everything happening in the field. We can use a framework that goes something like this: there are trillion-dollar companies like Microsoft, Google, Apple, and Amazon that have massive investments and resources to create many of these advancements. They have the algorithms and data which they have been accumulating for decades. These companies will heavily invest in this space, and some are already doing so. Additionally, there are many billion-dollar companies like Salesforce, Stripe, Uber, DoorDash, and Canva. These companies have marketplaces and ecosystems, utilizing cloud services provided by Microsoft, Google, and Amazon. They build applications on top of these existing infrastructure players. Any questions so far? Looks like we're good.
Arjun Saksena: Does this make sense? Now let's think about million-dollar companies. I'm referring to companies ranging from tens of millions to a billion dollars. There are hundreds of these companies compared to the hundreds of billion-dollar companies. On top of that, there are sub-10 million ARR (Average Recurring Revenue) companies, which are mostly startups. Once a company surpasses the 10 million ARR mark, it becomes more sustainable and can continue to grow. This is one way to categorize different types of companies. All of these companies utilize machine learning, AI, and Chat GPT, but the key difference is that trillion-dollar companies have not only the infrastructure and resources but also a large number of users generating data for them.
Unknown Speaker: For them.
Arjun Saksena: So, they are trying to supercharge existing widely used applications such as Google, Microsoft, and Amazon by injecting new advances in machine learning. If you're not on the research side, your primary focus would be understanding the use cases and customers' needs and applying machine learning to improve these companies' applications. For example, think of Excel, Google Docs, and the overall e-commerce experience on Amazon. Some companies, like Amazon, have scaled back their investments in Alexa as they look to the next five or ten years. On the other hand, startups need to gather customer data to inject this new technology. Larger companies, however, need to be more team players and less entrepreneurial to apply and learn about these technologies for enterprise-scale impact effectively.
Speaker 2: So, Arjun, where's the sweet spot currently, in your opinion, in this continuum between data and application?
Arjun Saksena: If you're entrepreneurial, the sweet spot currently is probably in the million-dollar company area. These companies have a fair amount of data and offer flexibility in utilizing business understanding to drive autonomously. They are looking for adjacent use cases and aim to move faster compared to more established businesses. Many people make the mistake of trying to sell a solution when they join or start a startup. It's not sustainable in the long run, as larger companies like Amazon already provide similar solutions. Startups need to figure out how to acquire customer data, while larger companies require a more team-oriented approach to apply and learn from these technologies. Larger companies have more structured systems in place, and individuals have limited autonomy compared to startups. Depending on your goals and preferences, different parts of the continuum may be a better fit.
Unknown Speaker: A lot of nodding heads there when you asked the question.
Arjun Saksena: Okay. Any counterexample? Anybody? Yeah, go ahead. Sorry.
Speaker 3: So, yeah, I don't have a question. What are the normal ways for these apps to collect data?
Arjun Saksena: Normal ways involve creating a use case where people willingly provide their data. Most startups in the ML space focus on connected data processing and offering insights. You need to identify the pain points of end users in areas such as sales, revenue, marketing, or engineering, where they have a lot of data but struggle to gain valuable insights quickly. It's important to understand the pain points of potential customers and create a value proposition that resonates with them. Data is valuable, but people will only share it if they trust your company and see the benefits of collaborating with you.
Unknown Speaker: That's very helpful. Thank you.
Arjun Saksena: Okay, I thought it was high-level, but good to know that. It makes sense. Anybody else?
Unknown Speaker: One more question here.
Speaker 4: Hi, yeah, I also have a question. So, if you're a startup, is it easier to get access to existing data and build your reputation or collect data by yourself? From my experience, I worked with an agrotech startup, and they said it was almost impossible for them to get the pictures of different types of fields and plants they needed. They installed cameras on existing tractors and combined and started collecting data by themselves. So what's the better solution for startups?
Arjun Saksena: Yeah, the better solution for startups is almost always to narrowly define the use case for which someone already has data but can't process it themselves. Starting a data collection business is very capital intensive, running a capital-intensive business in that case. Chasing data and asking people to give you the data without loss of funds is a bad business to be in because nobody will give you the data. You need to create FOMO by showing what they can get. All they need to do is give you the data. If you don't create that FOMO, people have a hard time visualizing it. It's not their fault because they have spent years trying to pull this data and create different systems, most of which haven't benefited the business. This has been the problem with machine learning (ML) in the last 10 years. Many companies have formed in the last decade to process data and provide insights, but very few of them have become big because it takes a long time to achieve precision recall, adoption, and ensure the models and data are right. Every time you do something, you need more data and longer timeframes. It takes time to stabilize, and people have gone through that cycle. So, they don't trust someone who claims they have the magic solution where they just need to give the data and everything will be provided.
Speaker 2: Just a quick note for those who might not have followed, FOMO stands for fear of missing out, just to clarify that.
Arjun Saksena: Yeah, I've heard. I'm on the wrong side of time here.
Speaker 2: Yeah, I think we have one more question. Go ahead.
Speaker 5: Arjun, thank you for taking the time to do this. It was really interesting the way you said that you have to create that fear of missing out for them to give you their data. But even after you manage to get the data, do startups like yours maintain ethical use and ethicality in the whole AI business? I think that's a real challenge we're starting to see with moderations on generative models like GPT. For example, if you give it names like Joe Biden or Trump, it won't generate any data. There's a lot of censorship. So, how does your startup deal with this challenge?
Arjun Saksena : Yeah. So I think there are two types. Great question two types. One is on the, you know, on the public domain, like the Twitter and the Facebook, and then there are business startups like b2b startups, where in our case, we are only dealing with business data. And that pertains to only you know, that form or that company that that requires this service. And there's less ethical issues there. Except that, you know, we shouldn't sort of merge one customer's data with another customer's data and there are many things already in place in terms of regulation of GDPR and PII and SOX compliance that we comply with, which is the trust building factor, you know, that, you know, most people don't want their data to be mixed up in any way with somebody else's data. So I think it's more about keeping keeping that separate versus any ethical issues so far. But I think when it comes to these trillion dollar companies, and they are the ones that are, you know, really, you know, jetting up with more of the ethical issues.
Speaker 5: I do agree with that. I also wanted to know, as a startup working in the B2B data, right? You might not have the professional expertise that a doctor would have when you're working with a medical dataset, for example. So how do you tackle that issue? How do you figure out what information you actually need in a business field or domain that you're not that familiar with? Do you hire experts, and how do you go about that?
Arjun Saksena: So I'll answer that question in two ways. Almost no startup will be successful if the founding team doesn't have direct domain knowledge or expertise. You cannot run a successful business or build anything where you yourself don't know the domain. Actually, there are two things you need to know. You need to have domain experience. You should have firsthand seen those things. And second, which is almost like a byproduct, you need to know enough people in that domain on a personal level. You can't, for example, if I'm not a doctor, and I want to start something in healthcare, and in the last 20 years, I don't know any doctors, it's a bad idea. Because when you go and validate something, let's say we came up with some stuff and we need to validate it, I don't know any doctors. I need to find people who know doctors and then set up a meeting to do the validation. That is just a bad idea. It's like wasting time and resources. Getting people on a call to validate your idea when you're that small is extremely difficult. So you need to have a group of people that you know who are going through the same business. You need to find something where you can tap into your network and everybody's in the same space. You can do this by taking a course, for example, in healthcare to build that network. Without that network, it becomes very difficult to find out which way to pivot and which use case to solve. You can only create FOMO if you deeply understand the problem.
Unknown Speaker: Otherwise, you're just guessing.
Arjun Saksena: And basically trying to sell a solution without understanding a problem. That is the biggest mistake that most startups make, and even large companies make. They continue to sell a solution. It's like Business 101. This happens 80% of the time. People get so enamored with the solution that they keep selling it without talking to customers. I'll give you two examples. One of the biggest examples in my view is Google Plus. It was rolled back 10 years ago. I don't know how many people ever used Google Plus, but Google saw that Facebook was cutting into their business and taking a lot of their users onto Facebook. Building Facebook is not technically very difficult. So Google decided to build Google Plus. They hired 300 of their best engineers to build it. They connected everything together and they thought they would at least get a large portion of Facebook's business. But in that process, they never asked anyone about what their problem was with Facebook. They were not trying to make a better Facebook. They were just trying to replicate what Facebook was already doing. As a result, three years later, they had no adoption because nobody was really interested in just using all of Google's services. They had a solution, they had the resources, they could spend $300-400 million on building it out. They persisted with Google Plus for multiple years before they realized their mistake. The challenges of Facebook were evident and have become even more evident in the last 10 years. If Google was smart or didn't fall into this trap, all they needed to do was get a few product managers to talk to 100 people, and they would have listed down the issues causing Facebook to change and rebrand every year, almost because their main product was not sticking.
Speaker 5: Does that make sense? I completely agree with you because when Instagram was blowing up and Facebook acquired it, TikTok did exactly that. They tried to acquire a lot of market share, and now it's become such a threat that the government wants to shut it down. It's already off governmental phones and everything. But that was really insightful. On top of networking, I would really love to connect with you on LinkedIn.
Arjun Saksena: Another example I'll give you is Alexa. Jeff Bezos, one of the smartest people in the world, started the fire phone and fire devices. Can Alexa be billions of dollars? It is a solution looking for a use case. Today, everybody has Alexa devices in the garage or somewhere else in the basement. There's no use case. There has never been a use case for Alexa and the fire devices in the last 10 years. Yet, they have invested more than a billion dollars creating this technology. They figured out that this is a technology and they are pushing it everywhere. For example, a friend of mine was running their small business for Alexa, and her job was to put Alexa in every hotel room so that when you walk into a hotel room, you can say, "Alexa, turn on the light." But the use case was the switches right there as you enter. All you needed to do was put your card in and it would turn on. If you make those kinds of mistakes as a startup, you are done. It doesn't matter what the technology is or how smart your engineers are. If you find yourself selling a solution that people are not buying, that means you are selling a solution that nobody really has a use for. Another example I'll give you is crypto. Huge hype around crypto. I've never heard anyone in my life telling me that they actually don't like the dollar, the current credit card system, or the monetary system.
Unknown Speaker : Cool anybody else?
Unknown Speaker : No more questions. Looks like nobody. Okay.
Arjun Saksena: So overall, today more than ever, building is easy, but selling is hard. As business people, what I have emphasized so far is that in the tech space, building is super easy. You can find three or four people anywhere in the world. Amazon pretty much gives you money to build stuff. They provide you with $10,000 or $100,000 AWS credits. Google does the same thing. Many solutions are free for developers to get started and create a website. Everything is super accessible, and there is no information asymmetry. Around the world, you can find people in Serbia, Vietnam, or anywhere, and they know as much about technology. So, the only thing I would say is that as business majors, building is easy, but selling is hard. Most people try to sell a solution, and it takes a long time for teams to realize that they're selling a solution. They often don't realize it at all. With the advances in ML and AI, with Chat GBD, this is becoming a bigger problem because people are so enamored by connecting these things and showing you magic. They say, "Hey, see how well I can do this," but they're not asking or pausing to ask what the problem is. It's much easier to do this in a theoretical setting, like in a classroom, but much harder when you're actually building something. I just gave you three examples of huge deals around the world where people are trying to sell a solution because the excitement around technology is so great. So, if you're trying to do anything entrepreneurial, even if you're building a product or a $100 million company, it's all about understanding the problem acutely. It takes time and a lot of analytical thinking to identify what is underserved. There is a Cambrian explosion in the number of startups that are similar. In every category, there are micro-niches, and even within those niches, there are 15-20 competitors. If you have an idea, you can assume that 20 other teams have similar ideas and are working on them. People start to believe, due to confirmation bias, that because they have this idea and nobody else has it, all they need to do is execute and build. So, they overbuild the product, de-emphasize learning, and create something that becomes very hard to sell.
And unless you are deeply technical or have a team that's deeply technical, you have nothing to show at the end of the day. Most of you are business majors, right? You guys are business folks. So naturally, you're not trying to build any deep tech that you can sell for creating intellectual property. Your go-to-market scale is what matters. People will come to you to understand how you can either create something from scratch or understand adjacencies. If you're working on a niche company, what is an adjacent use case? You should be able to articulate and build that, which is what product management is all about. It's about understanding the underserved persona and building for that persona. Do you understand the personas?
Unknown Speaker: Personas, folks?
Speaker 2: Different types of generic users, right? Yeah, yeah.
Arjun Saksena: One example I'll give you is, I hope this is clear. Any questions about the framework and how I think about things before I move on to a slightly more in-depth discussion on how to build a product?
Unknown Speaker: Looks like we're good.
Arjun Saksena: So far, so good. Is this making sense?
Unknown Speaker: Yeah, yes.
Unknown Speaker: Can you see my screen? Yes.
Arjun Saksena: So this is a high-level abstract use case that you will encounter. Let's say you have a question: if Microsoft had to acquire one company, what would it be and why? Okay, I'll distill it and break it down to save time. You can think of Microsoft as different businesses: the office business, Azure, Xbox, and many other large businesses over time. Each of these businesses caters to a few core users with similar needs.
Unknown Speaker: Needs.
Arjun Saksena: And these are the kinds of people that Office was built for. Office resonates with these personas. Here, I've outlined three different personas and an anti-persona. An anti-persona is someone who can be considered a persona but is actually not. Microsoft Office was not built for them. If you solve the problems for these users shown in pink, you can gain additional users and engagement. So any product you're building has two levers. You can either get existing users to engage more deeply and pay you more because you're solving their problem more effectively, or you can target an anti-persona and attract a different set of users to your product. To do this, you need a good understanding of your persona and anti-persona, including their fears, anxieties, hopes, and dreams. You should do this exercise for both the persona and the anti-persona. This is the kind of exercise that Google Plus probably didn't do in detail. They might have had some insights, but they likely didn't build based on a detailed understanding of the issues faced by their personas. If I'm an anti-persona, that presents an opportunity for me to either acquire or build a new feature set. ML and AI are capabilities that enable faster, better, and cheaper solutions for the needs of these users.
Speaker 2: Arjun, are you primarily anti-persona focused or are you focused on deepening?
Unknown Speaker: Are you focused on deepening?
Arjun Saksena: We are anti-persona focused. If it's helpful, I can explain in more detail, but it might be too detailed for you guys. But that's a simple way to think about it. Here's another similar example. I don't know if you've ever used FitPod or visited fitpod.me. It's a fitness app. We looked at six different personas: people who use it for free weights, people who want to use the demo or exercise log, calorie counters, goal setters, and variety seekers. But there are two use cases we identified: people who seek motivation to exercise and diet planners. These two would be considered anti-personas. The current FitPod app is not built to provide motivation or plan diets. That's the concept of an anti-persona. Is it clearer with this example? If you wanted to, you could add more features to the product, acquire another company, or partner with a diet planning company. You can integrate to provide these two services as long as you have a good idea and understanding of your current users and their needs. Once you identify that, you can look at multiple diet planner companies and select the ones using ML and AI to do a better job. But that comes after you've determined whether your current users actually need this feature.
Unknown Speaker: In gram, you're on mute.
Speaker 2: One of the things that occurs to me is that many of the folks in the room may be working for billion or trillion-dollar companies where projects are served up with instructions to go and do them. So, I assume your advice would be to question the use case because it may have just come through this pipeline of rebuilding solutions. How do you handle the politics of that situation if you're a junior person and someone higher up has assigned you a task? What are your thoughts on navigating the politics in that crisis?
Arjun Saksena: That's a fantastic question. I think it comes down to product discovery and better understanding the actual use. Let me give you an example. Suppose you're a dog lover and you join OpenAI. You probably don't have any intersection, but there used to be companies like BarkBox that catered to pet owners and their needs. If you have that business sense and work for a company that serves pet owners, you have a greater chance of success as a business person or product manager because you understand that domain. So don't just be excited by the brand of the company, but align your job with your core interests and expertise. Match your job to your core interests for faster growth. That's what I'm saying.
Speaker 2: So what I'm hearing from you is that we add value because we live in the world, while the algorithms don't. Our domain expertise and familiarity with how things work in actual use cases are fundamental. We add value by understanding what people want and need.
Speaker 4: Does that mean the end for computer scientists? Do they need to know more and be experts in their industry?
Arjun Saksena: Yes, a lot of the commodity code writing is under threat. Writing code will be taken away by generative AI. It's unclear what jobs will be created or stay, but one thing is clear: people will only pay for things they want, regardless of the technology. It's essential to understand what people want and build based on well-researched data. Many solutions will be built, but not all will be able to monetize. It's like 20 years ago when the internet came about, and many startups failed because people were not willing to pay for unnecessary things. Focus on what people need.
Speaker 2: From the perspective of fresh graduates, it becomes challenging as we don't have much work experience or domain expertise. We do have consumer experience, though. What advice do you have for building an early-stage career and being hired?
Arjun Saksena: Keep an eye on industry developments, follow closely, and stay current. Have a well-structured opinion about what has happened, what is happening, and where things are likely to go. Companies want to hire people who are aware of the industry and can adapt to change. Remain flexible and open to consuming new information and data. Your opinion should be based on the latest data. Companies want people who can pivot quickly.
Speaker 2: It's about staying current and finding ways to navigate and see where this conversation goes. It's exciting and challenging, but that's why we're here.
Arjun Saksena: When you go into an interview, be aware of what has happened and what is likely to happen. Companies want people who are knowledgeable and relevant. They want individuals who stay up to date with industry trends and are open to change.
Speaker 2: We have a couple of free minutes. Can we open it up for questions?
Speaker 2: What about folks that have dialed in? Anybody with a question? Just shout it out if you have a question. So I have a question. We've got these three or four major software houses: the Google ecosystem, Amazon, Microsoft, Salesforce. So given those comments that you just made about flexibility and the ability to pivot, which of the houses would you recommend? Is that although they're still important, are they going to survive? Sort of like deep familiarity with software houses? What are your thoughts on that and how does this connect to previous comments?
Arjun Saksena: I just was reading about that. I just shared this particular thing about the Big Five. Yeah, actually, you know, exactly. That conversation. Some are gonna say, somebody's not going to stick. So I think Salesforce is definitely under a lot of threat. So I wouldn't, you know, bet. They're an older company that is not that nimble. But I think Microsoft is very nimble. So is Google. Facebook is being retooled a lot. Many layoffs in tech are happening not because of running out of money or lack of profitability, but because teams realize that certain products are no longer relevant in the long term. They won't be profitable or used with these new technologies. There's a massive retooling of the workforce happening at speed. Vision engineers and some ML people are being let go in favor of those who know elegance and understand it. So I think as business people, the more you know about ML and its use cases and potential applicability, the better chances you have of getting hired.
Unknown Speaker: And, you know...
Arjun Saksena: If you think about all of these companies, especially in the Bay Area, it's like one big ecosystem. Most of these tech companies are run very similarly. There are cultural differences, but at the core, they run very similarly, and the skills are very transferable. If you're an ML or product or engineering person at Apple, you can easily get a job at Amazon, Google, Facebook, or elsewhere. They cross-hire heavily. So it isn't like 20 years ago when being part of GE would make it tough to go to Shell or Exxon Mobil. The underlying substrate is very similar. It's even more cohesive now than before. They all use the same cloud infrastructure, OpenAI, similar use cases. So it doesn't really make a difference. You're not disqualified by working for a specific company. That's why many people leave these companies and start startups. Hiring is fluid.
Arjun Saksena: So I wouldn't worry about individual companies. I would focus, not even worry, just focus on knowing more about ML and how these things work, what the trends are, and keeping up with that. Remaining relevant.
Unknown Speaker: Last question, anybody? Oh, Maria's got it. Last question.
Speaker 4: I have one more question. So if you were at the beginning of your career right now, would you join a big company, like a huge company, or a smaller startup if you want to follow the technology trends, if you want to be ahead of the world?
Speaker 4: I have one more question. So if you were at the beginning of your career right now, would you join a big company, like a huge company, or a smaller startup if you want to follow the technology trends, if you want to be ahead of the world?
Arjun Saksena: Yeah. So in my first job, I joined a nine-person startup, and it turned out to be a really good thing. But I wouldn't do that again. Because small companies require you to have a lot of emotional intelligence to do business in a startup. There is so much failure associated with it that you need to be emotionally mature to handle it. When you fail, if you join a startup and it fails, you tend to take it personally, which brings you down. But a company like Microsoft or Google or something else has all the things that make you keep believing in yourself. So it depends on how much self-belief you have to keep you going. I think there are going to be a lot of failures in the next 5-10 years because a lot of things are going to be tried, which means that people who have worked at Microsoft for 20 years have that experience to make them feel free to handle the downs in life.
Unknown Speaker: See, we've got one more question from KJ.
Speaker 6: How's this? Because y'all, I have one question. Like for a CL CRM company, how can you build trust with your customers? Because for your customers, they share data with you. So how can we gain their trust and convert them? Like, use the data they share with you and provide a solution for them.
Arjun Saksena: So let me repeat that question. You're asking for a PLG company, how can we get data for a CRM company? How can we...?
Speaker 6: You can't get it as a charter from another company because they need to share data with you. Because industry data, your data, is important for other companies. So how can they trust you? Or why do they trust you?
Arjun Saksena: It's more about the use case, as I mentioned earlier. It's not going to be straightforward. They have the data because they want to generate revenue from it. If you find people who have data but are stuck because they are unable to generate revenue, that's your opportunity.
Unknown Speaker: Bring them to you, yeah.
Speaker 2: I think we've covered that. I think that's the end of the questions. We genuinely appreciate your contributions here. Thank you so much. Your insights were very insightful. We really value voices like yours in our classrooms. And thank you so much.
Arjun Saksena: Yeah, thanks for inviting me. This was fantastic. Feel free to reach out to me on LinkedIn or elsewhere. I'm happy to answer any questions.
Unknown Speaker: Thanks so much. Thanks, everybody.