The AI Job Myth, Intelligence You Can Trust & Government-Controlled AI | SS #33

This week on Software Sundays, KD breaks down one of the biggest questions in technology today: will AI actually replace skilled professionals, or are we dramatically underestimating the value of human accountability, trust, and judgment?

We start with new claims that millions of workers are at risk from AI-driven automation and why software engineers, accountants, and legal professionals may be far more resilient than many headlines suggest. KD explains why trust-based professions could require even more human oversight as AI adoption grows. 

Next, we dive into Anthropic’s controversial decision to quietly route certain requests to less capable models without informing users. What does this mean for trust, transparency, research, software development, finance, and other industries that rely on AI-generated insights?

We also explore proposals for governments to take ownership stakes in major AI companies, what that could mean for citizens, universal capital, national security, and why AI is increasingly being treated as a strategic national asset.

In this episode’s Q&A, we cover the skills every entry-level software engineer should have in 2026, how organizations can secure open-source software, the differences between SaaS, PaaS, and IaaS, why one-on-one meetings matter, and what data centers actually do beyond powering AI.

We close with a reminder that the bar is constantly rising and that long-term success belongs to the people who continue learning, adapting, and improving.

CHAPTERS: 

00:00 Introduction to Software Sundays

00:43 AI and Job Automation Concerns

08:01 The Role of Humans in AI-Driven Fields

13:42 Government Involvement in AI Companies

18:55 The Risks of Over-Reliance on AI

21:00 Entry-Level Software Engineering Skills for 2026

24:31 Securing Open Source Software

28:00 Understanding SaaS, PaaS, and IaaS

31:03 The Importance of One-on-Ones in Organizations

32:50 The Importance of Data Centers

37:44 Continuous Growth and Improvement

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Transcript

SPEAKER_00 0:00

We are trying something new today. Welcome to Software Sundays. In this series, we have high-level conversations about technology and the impact that it has on our community. I want to make sure that you can walk away with the tools that you need in order to grow your income, become an owner, and help shape what happens next. If this is your first time tuning in, you are in the right place. Thank you for being here. And if you've been rocking with us for a minute, it is great to have you back. Welcome back. Software Sundays is for informational purposes and is not professional advice. The views expressed are my own or those of individuals quoted. The topics discussed may or may not apply to your specific situation. Please consult your own legal, business, or tax advisors before making any decisions based upon information found in this show. Let's get started. This week, some very interesting topics we're gonna cover. First, half of London's workers are in roles where AI can automate a significant portion of their tasks. So this is a report coming out of a Bloomberg article where they're basically making the claim that there are over 50% of the people, I think over 3 million citizens are in roles that are at a high risk or a very high risk of being automated or replaced by AI. Interestingly, a high number of those roles are in the legal field, software development, and even accounting, which is something I strongly disagree with. I actually agree that there are a significant number of activities that developers do, that accountants do, that legal professionals may do. I don't know much about legal, the legal field, that there's a significant amount of things that are being done on these teams that can be automated and can be improved through automation. But in terms of replacing the workers in these roles, I don't think that's going to happen. Or I think that'll happen in the short term, but over the long term, we'll start to see a rebound in the effect of AI because when we're talking about software engineering, when we're talking about law and finances, these are roles or these are areas of a business, of a society that require a high amount of trust, a high amount of accountability. And it has been made incredibly clear by almost every government that systems, software systems cannot be held accountable for the impact that they have on human lives. That means someone on your team, someone in your staff must be accountable, must be able to explain why this software, why this automation, why this workflow is taking certain decisions. And that means there needs to be someone in place on the teams to do that. And so that's one reason that I don't think it's ever going to replace the full workforce in these roles. Additionally, I believe that we're going to need more of the people in these roles because let's even start with just law. If we think about the amount of lawsuits that are going to be happening, because people now are going to have access to more readily available information, right? People know are going to more easily know what their rights are. They're likely to attempt to pursue more lawsuits, more legal support. That means more lawyers are going to be needed. Because even a few months ago, a defendant in some law room tried to have an AI agent talk to the judge, and the judge shut it down. Right? We're not doing this, you're not making a mockery of this, of this proceeding, right? So that means you still need a person in place to act on your behalf. And a lot of people are not going to have those discussions and communicate on their own. So they're going to recruit a lawyer to do that. Accountants. When we're talking about large amounts of money, large sums of money being managed by a piece of software that can make mistakes. At a certain level, you're not letting this software just make a trade, right? You're not just making letting this software uh buy anything that it wants, right? You might have it recommend some purchase. You might have it do some research before the purchase, and then you go verify, you know, some of the claims that it's made. But you are going to require a human to actually take responsibility for the decision being made with the money that is being spent. So you're going to need these humans in that role. And now, another thing that was interesting about the article was that in nearly every role with exposure to AI, there is a belief from employers that they're not, they're no longer looking for like an okay person in the role. Like you don't need to just be a graphic designer. We need you to be a graphic designer, a great copywriter, a videographer, and be able to edit those videos later on, right? So they're almost looking for people that are multi-talented, very dynamic, and able to learn very quickly. And yes, that is something that maybe everyone right now doesn't have those skills. But at some point, when they start using AI more, when they start embracing the tools that are available, they're going to be able to become those dynamic people, those renaissance people that can do everything with no time, right? And that's going to again end up with us having more of these roles being filled. We're going to have more of these people being necessary. And that's going to be, and that's going to look like more opportunity for these fields and these spaces. Another interesting part that I just want to highlight that I don't feel like people are totally understanding is that let me just read this quote. Much of the boring audit work of reviewing financial records or checking transactions can be automated, leaving human judgment primarily for the final sign-off, said Seamus Ray, a private investor in AI businesses. Let me repeat this. They are someone who will benefit only if you believe them. And that does not mean that it's necessarily true what they are saying, right? I don't think it's true that these roles are going to be 100% automated. It's not going to just be a sign-off process and you just click a button and everything always works the same way. You're going to need to review that process uh periodically. You're going to need, well, you're going to need someone to review that process periodically. You're going to need someone to, you know, design the process. You're going to need someone to sign off, yes. But when I say sign off, you're going to need to sign off on different aspects, right? Human in the loop doesn't just mean you start the loop and then verify the loop completed. Human human in the loop means you start the loop, and then as different decision trees are created, as different conditions can be applied, you continue to make more decisions and continue to, you know, allow the automation to make the next step inside of the process. And again, you need people for that. So my only tip, and the reason why I'm highlighting this, is for the people that are feeling discouraged about their future employment opportunities, I understand that right now we're just in a very specific phase in the labor cycle due to AI being new. They don't actually, and I say they, these employers and companies don't actually know how it's going to impact their productivity or the quality of the products and services that they provide. They know it will impact it in a way, and they're trying to, they're trying to apply it in their current scenarios, in their current environment, but we're none of us are really a hundred percent sure where you need a human versus where you can really rely on the technology. So the opportunity for you will be there as long as you're ready. So continue to develop your skills and be prepared for when the opportunity calls. Additionally, we got some on some other news. Anthropic backtracked on a policy that was quietly sabotaging the work of researchers. So Anthropic recently released the Fable 5 model, which is one of their most powerful models up to date and based off of their Mythos model. It's a publicly available version of it that has had a few, let's say, safeguards put in place so that it doesn't present a significant danger to the public and public institutions. One of the features, quote unquote features, or uh limitations of the model, depending on how you're looking at it, is that it would route a request that it believed was against the company's policies to a lesser model, a lesser performing model. So something that's not as intelligent as Fable 5 when it had to answer those questions that it felt were not, again, aligning with the terms of service. But it wouldn't actually tell the user that it was doing that. And there was a lot of backlash from the public for this policy because it was think about you're asking someone for advice on some topic, and you expect them to give you their best answer. But instead of giving you their best answer, they give you an answer that they consider okay. But it's not your best option. This is literally what they were doing, and it really highlights the dangers of outsourcing intelligence to third parties because you're relying on them to operate in a trustworthy manner, you're relying on them to be fair, you're relying on their policies to align with how you expect the world to work. And that's not always going to be the case. Even speaking, even uh another part of this is the fact that uh, well, before we even get to that, like part of the risk in different areas, again, going back to uh the our first story, if you have a software developer, if you have a legal or a law professional or even an accountant, right? Let's say you're attempting or having AI attempt some coding tasks and you needed to be creating this code for a regulated environment for a very a very sensitive project, whether it could be the software inside of a fighter jet or the software inside of some type of medical equipment. Imagine the response that you're getting, that you get isn't is like a poor response, but you're expecting a high quality response and you're trusting that the response was high quality. And so if you don't have any type of quality assurance processes in place, or you have minimal quality insurances in place because you, again, think that whatever you got from this machine was going to be the right thing, the best output, not only are you paying a lot of money for you know limited intelligence, but you're paying a lot of money for this limited intelligence that's also wrong. And so the product that you create from that intelligence is also going to be wrong. It's not gonna do what you really needed it to do. Then imagine if you were in a financial industry and you had to make this a decision on how to invest a particular amount of money. And again, they routed you to a model that is not the best model, then the decision that you make from that model is going to be less than efficient. And there are numerous industries and numerous fields where having that type of quiet, poor performance is going to directly impact the quality felt by the clients or the customers of that business. So, and this is from Anthropic, who has painted themselves out to be one of the most ethical and fair and transparent and open AI labs, right? So imagine what the other labs are doing. Imagine what their terms are doing, what their systems are downgrading, and when they're downgrading them. But even more important is the fact that when you rely on these technologies and you think they're working a certain way and they don't, without you knowing, that can it can really just lead to less trust, especially if your intelligence, your human natural intelligence is not where it needs to be. And moving on to our next story, the US president is considering having the government take a larger stake inside of these artificial intelligent giants. When we're talking about anthropic, open AI, these companies, the government is considering a policy where those entities are invested in by the government, which creates a type of fund that can be used to provide dividends to the citizens of the country. Part of this is in an attempt to combat some of the negative effects of these models actually, you know, taking jobs from people, for lack of a better term, right? Or impacting the workforce in a significant way, right? And giving people a stake in the technology versus just being a victim to the technology and the changes that it created inside of the economy. And part of this is a way, in my opinion, for those, for the government to have more control over these technologies and these companies. Because investors in the US can vote on how a technology or how a company is going to operate. And if the company owns and operates a particular technology or service, they can have a pretty strong impact on how that technology will be uh operated. So there's that opportunity there. But it it makes me think about whether or not when we say the government will take a stake in these companies, does that make them like Fannie Mae or other government-sponsored entities where you are now accessing taxpayer dollars, right? So does that mean that they can get bailouts? Does that mean you print money for these companies when they do poorly or when they're at risk of failing? Does that mean that the government can control exactly what they do, like everything that they do? Or does that mean that people vote for the government to control everything that these companies do? And and how would that flow really work, right? Because I don't think today I could vote to change how Fannie Mae works, even though it is a GSE. So I'm not sure about that. I'm not sure about how that flow would work, but it does remind me of something I spoke about a few weeks ago with the governor of California having or researching a similar policy, basically seeing how can the government take an active stand to stop or to reduce the negative impacts and negative consequences of AI going into the market. So maybe that's through upskilling so that people have the skills that they need to work in these companies or work alongside AI, but also potentially having some type of universal basic income, which is a very similar it's similar, but different because similar and different compared to universal basic uh capital, which is more an investment versus universal income UBI, which is more like just direct payments. UBI, the question there is where does the money come from? UBC is more like the money comes from these companies and the revenue that they generate. So that's where it comes from. So how how you run it is going to be a very different practice. But there's definitely a lot of conversation in something like this. But it also makes it more important to understand or it highlights another story that recently happened with the US telling anthropic that they have to limit the people or foreign nationals, foreign people limiting their use of the best models. So they're fable five, limiting access to fable five models and mythos, which is very interesting because they're not even investors yet, they're not even technically in control of this company, and yet they were able to say that you cannot sell this software to these companies. And that's not something I've seen from the software level in the past. Like we've definitely seen the chips, the hardware, be something considered like be considered a national resource or weapons, like you know, rockets, certain ships, drones, those as sovereign national security resources that you know you can't just sell to enemies of the state. But the fact that we have expanded that definition of national assets to include software is very interesting. And it speaks to how important AI is going to be and how important it is today in the development of any nation and how that nation is really competing in today's society. And I think we'll only see more of that if the government decides to take a more a more financial, a more significant piece of these companies. So that precedent is really, we're gonna start seeing like what that really means and how much control the government will seek of these companies. But the theme that is worth highlighting and worth thinking about is the fact that I personally think we're moving way too quickly towards relying on a technology that is just software. And the software is really good software, but at the end of the at the end of the day, it is still just software. And that means the software is not infallible. That means someone actually developed the software, someone is responsible, so a human who can make mistakes has created this software that we're becoming incredibly reliant on. But I also know that we're not going to backtrack, we're not going to slow down the amount of innovation happening inside of this race, right? We're going to continue to improve the models, we're going to continue to deploy these models into different areas of the economy. And it's going to continue to have an impact on each and every one of us in our lives, in our business, our families, everything. And so the answer is not just to complain about what AI is doing to the job market or what impact AI is going to have and how it should be regulated. An important thing to understand, the important question to answer, is how do we take a more active role and a more active stance in how this technology is being deployed? How do we make it more equal when we're developing these tools so that everyone, well, not necessarily just everyone, but that more people are providing their just judgment and wisdom on how the technology should be deployed and how it should be developed. Because right now, if you only have a select few people managing the innovation, managing the development, then we don't see and we don't get the full benefits of having a more diverse conversation around this technology, which is going to be incredibly important, especially given how widespread it's going to become and how much of an important part of society and economy is going to become. So if you want to dig deeper into any of these topics, join BLI University and we will help you explore these and other topics and the opportunities that they create for you and your family. Every week we are discussing the biggest changes that are happening inside of this the technology industry and behind the scenes with a group of builders that are driving the revolution. So if you're interested and you're ready, get started today. We're gonna jump into our QA section now. First, what are the entry-level software engineering skills in 2026? So when I think about a junior developer and what I would need for them to know and understand when joining my team is very first thing, you need to read and understand code. You need to understand how to write and read code. So being able to create a script and update a script or some type of software and the code for that software isn't going to be necessary. Whether it's just knowing how to update a logical conditional statement, knowing how to update the variables inside of a piece of software, knowing how to change a configuration, right? I need you to understand how to do that. I need you to understand how to read another person's code. If I give you a code base and say, you know, explain to me what this function is doing and write some documentation on this code base. I need you to be able to understand that, understand the like how to. To get into every level of the code, how to explore the code base, right? Not just sticking it in one file, but knowing how to move around, knowing how to look up and look into the helper functions, knowing how to look at what functions does this function call and how the actual stack trace might work without an actual exception happening, right? And us having to manually verify that trace. We should know about that. We should also know about software architectures and the different components that could be in place, right? You should know what a database is, you should know what an API is, what a cache is, what the cloud is versus on-prem, and how that all sits inside of a products or a services architecture. And you should be able to point to them and say, this is where this should belong, this is where this problem could be if we're noticing these types of challenges, right? You should be able to document that and understand how to read an architecture diagram with these components inside of it. And then you should be familiar with an IDE, a coding assistant, GitHub, GitLab, or some other source code management tool. And at least one cloud provider is console, right? You should understand how to log into an AWS console or an Azure console or a G Cloud console. And I'm not saying use all of the services in that console, but at least be familiar with kind of what they do, what services are available, the names of some of those services, at least the most important ones, right? Like how to spin up a VM. What you're not probably not going to ever spin up a VM, but you should be able to at least know how to do it. Or you might be able to look into the logs for a particular service or application. Like you should at least be familiar with exploring that console. Because we're not expecting entry-level software engineers to know everything, but you need to know enough to not be lost if we send you on a mission or we send you on a task, right? We we shouldn't have to hold your hand through everything, and it shouldn't take too long to get you up to speed on what our current team is working on, or you know, how we're managing our processes because you're familiar enough with most of the tools that we're using, or at least the you're familiar with the category of tools that we're using. That's something that you have to keep in mind when you're going into any team. Like make sure you have those kind of fundamental skills and that fundamental context and knowledge so that when you jump onto a new team, you're not lost and it doesn't seem like you know you're out of place. How can we secure open source software? The first thing I want to do is highlight how difficult it is to make open source software 100% secure. The best you can hope for is to reduce the risks of that open source software negatively impacting your products and your customers. And the way you can do that is by, in an enterprise environment, having some type of private repository where you actually store any dependencies, whether it's an image that you trust or a dependency, like uh some type of package, right? Some library that you and your developers and your teams use. You want to have a repository to store all of these resources that is trusted, that is auditable, that is managed by your team. You're doing that because every day there can be a new update to an open source library or some open source project. And those projects can be hacked, they can be compromised very quickly too in some time, in some cases. So you want to make sure that you're never relying on those third parties to be working and functioning the way they're supposed to work and function. And you want to have some controls in place to manage and monitor when things change on those on that side, right? Open source software is one of the benefits, is again, it's open source, right? So anyone can look at the code. But when we're saying anyone can look at the code, that means anyone can deploy an AI system to identify a vulnerability in that code, right? We've seen it more recently with, and one of the risks with Mythos was that it could look at a project and identify a dozen ways to compromise that software. And most projects rely in some form or fashion on some piece of open source software. So that surface area of attack can open up the door to zero-day vulnerabilities that could be used to compromise your systems, but it could also open the door to unreliable software. So you also want to have a process for creating a SBOM or a software bill of materials that can be used to manage the actual like dependency list for a particular piece of software or particular version of your software application. And that SBOM is really going to determine or help your team determine when you're exposed to a specific vulnerability or a specific compromise of a package that you use, because you may not even be using the version of the dependency that has been made vulnerable. So you need to have a process in place, some type of management system in place to identify that or make it more easy for you to identify that. But just understand that open source software is inherently more risky because you're relying on just the ecosystem to just be secure. And you can't really rely on that because it's open source. I wouldn't even just say it's because it's open source, because it's more that it's open source, and so there's no the controls that your company or your team actually has for how you protect and manage vulnerabilities, those controls may not be in place for this project. And so that represents a risk. And you're not gonna 100% get rid of the risk, but at least you need to be able to manage and identify that risk and what you can do to mitigate it. What is the difference between SaaS, PaaS, and IaaS or software as a service, platform as a service, and infrastructure as a service? So a software as a service product is 100% managed by the vendor. And that means there's almost no configuration needed by the user in order to get started with that product. So if you're thinking something like Gmail or some other web-based email browser, or Miro, which is a tool for creating diagrams and shared diagrams on the cloud, or even Slack or Datadog, right? These tools are all SaaS products because for you to use it, you don't need to do anything special, you don't need to configure it, you just need to create an account and get started using the services. And they the vendor manages all of the infrastructure, they manage all of the operating systems, the fit like everything to make sure it works for you. All you have to do is use it. And SaaS is going to be tools that require the least amount of responsibility from a vendor, from a user, because all again, all of that's being managed by that vendor. And then we have platform as a service, which is any application or any service that your team can use to build on top of. So if we're thinking AWS Lambdas, right, the Lambda serverless functions are a platform that you can deploy your apps on, and you don't have to manage the infrastructure, you don't have to manage the operating system. You just deploy the code and you can trust that the code will get ran. If you think about MongoDB Atlas or other managed service providers, right? They will manage everything and they will provide this runtime to you. And then you can use this runtime in order to build your applications, and you can just configure it a little bit more to actually meet your application's needs, but there's not much you need to do from an infrastructure standpoint. So Classroom as a service is a little bit more hands-on than a SaaS, but it's less hands-on and requires less management than infrastructure as a service. Infrastructure as a service is going to provide you the most control compared to these other options, but also the most responsibility because the infrastructure as a service is going to require you to manage the operating system. It's going to manage to require you to manage the uh versioning of that operating system. And a great way to think about like these different models is to understand that there's a physical layer, networking, data storage, operating system, but the operating system is another layer, right? Then you have the runtime, the data, and then the application, right? When we're thinking SaaS, everything up into the old apps application level is being managed by that vendor. And then infrastructure as a service, you're getting everything managed up into the operating system, right? And so when you think about it in terms of these layers, it makes it easier to understand the differences between all of them. Should I schedule one-on-ones inside of my organization? So it is incredibly important to connect one-on-one with different members of your team. And you can get a large benefit from connecting one-on-one with people outside of your team, especially when you have connections with people that are either downstream of your team or upstream of your team for how decisions are being made. It'll allow you when you're having one-on-ones and you know, frequent run-up one-on-ones, it'll allow you to have better context about the business and across business units. You're not just understanding what your team is doing and what your team is working on. You're able to get some context and clarification on what your team is working on and how that's affecting other teams across your organization and what things are in the pipeline that are going to affect your team in the next couple weeks, months, or years. Right? So it's very helpful to have these one-on-ones. It's very helpful because it when you can build a relationship across your team and across your company, you get to just have more access. And that access can make you a more important person in the company because you can become the go-to. You can become the person that knows more about the things that are happening. You can become the person that people call when they have a question, and you can connect the people that need to be connected. And that's very beneficial in any company, no matter the size, no matter the industry. Another important reason that you should be scheduling one-on-ones is to make sure that building relationships and having a you know, having a good relationship with your team. And the way you can do that, you can do it virtually, but you can also do that in person. But it's definitely something that you should definitely invest some time into. So there has been a lot of debate about data centers and whether or not we need more of them and what you know what impact they're having in our environments. So I just want to highlight the value of a data center and kind of where they're used, what they're being used for. So data centers for years have been used to store large amounts of data, and that could be videos, images, text files, and for different applications. So that could be inside of your Instagram, Instagram using a data center or Netflix or your bank, right? All of them are using a data center to manage your data and the things that you're working on and make sure that you're provide they're providing uh the services that you rely on. If you're they're also used to run workloads. So if you're thinking about when you're streaming videos, whether it's on Netflix or Hulu or whatever platform you know frequent, that stream, that service is provided directly from a data center. Even when you're playing online games, with your playing on on the PlayStation Network, right, and you're playing against people over the internet, multiplayer, that interaction again is managed through a data center. And then even when you're making transactions, whether you're buying something at Chipotle or buying something online from Amazon, those transactions go through multiple data centers in order to make sure that the thing that you purchased is the thing that, and basically the whole process starts, right? That the company gets the order, that the banks transfer the money so that you know it can start the process, and that you know, we create a new order with your shipping provider, whether it's FedEx or UPS. All of that is going through a different data center to make sure that the thing that you get or the thing that you want actually comes to you and comes to you on time and reliably. So data centers are for more than just AI. AI is one of the more recent use cases for data centers, right? We're running more AI workloads on these data centers, but we were using them and we will use them, whether AI is the majority workload or not. And the reason why we have so many data centers is one, because there's so much data, there's so many transactions, right? And one of the benefits, or not even just one of the benefits, one of the like physical limitations of computing is that the closer you are to the data center, the faster your response will be. And so most companies optimize for speed, so they're gonna optimize for having more data centers located nearby their users. But another reason why we have multiple of them is that it's for redundancy, right? If your data center gets knocked down, whether it's through an earthquake or some type of hurricane or fire, you want to have some copy of your workload and of that data because otherwise, once that data center goes down, the workload will go down with it. And we're talking about workloads from banks, hospitals, and other very important areas of our society that we really cannot afford to have go down. So these data centers will copy data between different sites. They're going to, again, provide redundancy just in the case of an emergency. I one of the reasons why I think that's important is if we're thinking about the value of the data that we're storing and the benefits that it'll provide, right? Or let's even think about the civilizations whose history have been lost to time due to either their like temples being destroyed or their libraries being destroyed, where there's no history of that society or that civilization, but there's not enough of the history available. I think the fact that we have redundant data centers is a way to combat that, right? Like we'll we'll never have to go back to nothing because the data about this bank or this book that's not just stored in one place, that's stored across different regions. And no matter what happens in one place, we'll still be able to access it in the other place. That's one of the benefits. So that's why we still need multiple data centers, and it's not like a good thing to just have things running all on one site because that just introduces way too much risk of failure in case that one dependency fails. And these data centers are what we think about when we think about the cloud, right? They are sites that have thousands of servers, thousands of cables running through them to make sure that any company that wants to use them is able to actually use those data centers to provide their services to their customers or their constituents. That's what we have for the QA for this week. The goal of our QA is to make sure that you have all of the context that you need in order to make intelligent decisions inside of your roles and when thinking about technology and what you can do with technology. So I hope this has been helpful to you. A quick mindset reset before we get out of here. I just want to remind you that it's never okay to do the bare minimum and to remember that the bar is going to be raised every day so that what was once good or even great could tomorrow become just average. And that means you have to keep up. And that also means that value is relative to the current standard, not yesterday's standard. You can't think you're going to continue to operate doing what you did a year from a year ago, a year from now, because everything may have changed. There are new tools being developed, there's new knowledge being discovered, and there are new techniques that can be applied. People are moving faster and better every day. And so you have to compete and understand that you're competing inside of this environment that is constantly changing. And yes, that can be difficult for some people, right? It is not easy to continue to grow, to continue to develop, but I don't mean you don't do it, right? The people that continue to consistently improve, even 1% a day, those people are going to have a much higher chance of success and long-term success than those that just want to rest on their laurels or rest on the successes that they used to have. We gotta continue to perform against the current standard that is available to us. We can't think that we've ever done enough for too long. It's okay to take a break, especially when you've hit a new milestone. But understand that that new milestone now becomes the base. So you can't ever drop below that. You don't ever, you shouldn't ever even want to drop below that. So I challenge you and I I would say challenge and even just support you as you keep learning, keep growing, and keep sharpening your skills so that you can increase the value that you bring to yourself and to your community and the people around you. That's all we had for our message today. Quick announcements before we jump out of here. I want to say happy belated birthday to my cousin Ayana. I want to wish you much success as you continue to grow and continue to develop your creativity and just use it. Continue to enjoy your life and the things that you are creating and drawing and building as you go through your life. So, you know, enjoy your birthday. Hope you're feeling great, hope you're feeling amazing. Ayana, not Ayana, uh, Anaya, happy birthday to you. You are growing into a young lady and a very intelligent and thoughtful person. And I hope to see you continue to grow into that person that you want to be, and that you continue to become the person that is, you know, very, very uh, you know, strong-willed and you know, continue to do what's right for you. So good luck in your life and all the things that you are doing. Uh, Brianna, happy birthday to you. You are a great mother, a great woman, and a great cousin. So I appreciate you. I hope you are feeling great in this year. I hope you are not letting all of the challenges that are challenging you, you know, dissuade you or discourage you at all. I know you're not, you're too tough for that. So, you know, just keep being you, keep doing great. And definitely want all of us to go hang out sometime very soon. We gotta can we definitely need to, I know sometimes, my four, we definitely need to continue our summer adventures. So look, look out for some plans from me. I will be sending some information your way. And this week is Juneteenth, so my only message is black men and women deserve a lot. And let's take this break that we rightfully deserve to enjoy, to reflect, and just have some respect for the people and the the nah the ancestors that we came from, and just have respect for where we are today and how far we've come. Because we have come very far from where we started, and we are continuing to grow and develop as a people, and I love to see it. And next weekend is the I Self-Express 2 annual self-esteem awareness walk in Harlem. Definitely come out. I am going to be sharing some thoughts and some experiences that will be very valuable for the community to learn about and listen to. So I would hope and love to see all my people come through, show out, and show some love. So, that being said, enjoy your week. Happy Sunday. Uh, be great, be happy, be healthy, and I will see y'all in the next one. Peace out.