Why 90% of People will fail to get a Cloud Job – Specialize or get left behind!

Most people believe that to be successful in the tech world today, you need to learn all of the latest AI tools. In this video, I'm going to explain why that's not the case and why what you actually need to do is a little bit different. You need to learn some very specific cloud and AI skills to specialize in specific areas that are in the most demand today. And I'm going to help you understand exactly what those skills are, where the opportunities are, and the exact pathway that you need to learn, specialize, and become ready to land positions in the industry. My name is Neil Davis and I've been working in tech for over 25 years and over the last several years, I've been running Digital Cloud Training and I've taught AWS to over a million people worldwide. So, let's get into it. Now, if your newsfeed is anything like mine, it's all talk about all of the different AI tools. We got ChatGPT, we got AutoGen AI, Microsoft Copilot, we got agentic AI tools like Open Claw which starts to automate all sorts of things for us, Cursor development, NA10 for automation workflows, Claude Code of course, Wind Surf, Crew AI, Perplexity, Gemini, and of course many, many more. So, there's all sorts of stuff happening in the world of AI today, huge amounts of development. One of the challenges with that is it's really hard to keep up with what's going on. It's really kind of like the wild west today in the world of AI. We've got all these new tools coming out, it's changing super fast, there's tons of volatility, some products are going to become obsolete, some companies while others are going to become very, very successful and perhaps be with us for the long term. But trying to keep up with that is very, very difficult. So, the good news is we actually don't need to learn all of these tools. Some of them are very useful to us, but learning all of them is just not necessary or trying to keep up with the constant change. Cloud

computing is the foundation of everything. AI runs on the cloud. So, basically all those tools I just showed you and many, many more are running on the cloud. Now, they might be partially or fully in the cloud, it really depends, but most of them have been developed on the cloud. So, cloud is the platform on which those services actually run. Applications run on the cloud. So, most of the SaaS applications we're using from social media to Netflix to productivity applications, they all run on the cloud as well. So, underneath them is always AWS or Azure or Google Cloud Platform. Startups have of course loved the cloud, always have done because it gives them that ability to get up and running and scale without huge capital expenditure. And enterprises are migrating to the cloud and that's where the biggest spend is in technology, that's where most of the capital is. Enterprises now are moving to the cloud faster than ever before because they want to start building AI-enabled applications in the cloud. So, cloud is essentially like the layer everything depends on and the reason I've got a picture of a power station here is because AWS and Azure and Google Cloud Platform, they're really like the power stations. Everybody needs the energy they produce, in this case it's kind of technological energy, the power of the cloud platform to be able to build on top of it. Power stations always get paid. Good times, bad times, people always need power. Sometimes more, sometimes less, but we always need that energy. So, they're going to get paid regardless. Doesn't matter if half those companies go out of business and other ones sprout up in their place because AWS will always get paid. So, AWS will continue to grow even if there's this massive volatility in the AI space. Now, AI is the biggest

driver of cloud and job growth today. So, the reason that we're seeing such growth in AWS, which has accelerated in recent times and the same is true of other cloud platforms as well, is because of the amount of companies that are now quickly moving to the cloud so they can build their AI applications where all the capabilities already exist. So, we see companies investing huge amounts of money, tens of billions of dollars into these cloud platforms. We're seeing huge amounts of money being spent on compute with sometimes over 100 billion being projected over the next few years and over 80% of enterprises are increasing their spending on cloud in order to support AI workloads. And AI workloads alone driving a 20% increase in compute requirements in the cloud. So, we're seeing this growth because of AI. It's really good for cloud computing. Now, when we look at the job market, there's some really interesting data coming out. We're seeing that a lot of organizations are looking to increase their head count this year, 2026. Nearly 2/3 of technology hiring managers say

it's more challenging to find skilled professionals than it was a year ago. So, what we're actually seeing today is that there's a skills gap. The skills that companies need are not available or there's not enough people with those skills in the marketplace. Specialized skills and depth of experience are extremely important today. According to this statistic, there's 1. 6 million open artificial intelligence roles with only 518,000 qualified candidates globally. So, a big gap and in specific areas like machine learning, we're seeing more than 80% growth in the number of jobs advertised in the last year, while there's only a 20% increase in the number of people qualified to do those jobs. So, the gap is widening, which is why the wages, the compensation for those roles is going up all the time. Cloud architects as well remaining one of the top five most in-demand IT roles for 2026. Skills-based hiring is now a really important thing. You're going to hear this term more and more. Skills-based hiring essentially means that companies need to ensure that those people that they hire are the ones who can do the job. They can actually they actually have the capability to do the job from day one. So, it's less about looking for certifications, what job title you had in the past, and more about making sure that you actually have the capability to hit the ground running straight from the beginning. Tech salaries are projected to rise by 8 to 10% in 2026 according to this. And highly specialized positions like cloud security architects, DevSecOps engineers will get even more compensation due to niche expertise. Now, specialization is a theme here. It's very important today to have a specialized, differentiated skill set that aligns to specific problems that businesses have. So, you bring the capability they need to achieve their goals. In 2026, the tech job market is shifting away from broad-based hiring toward highly selective AI-centric growth. And AI integration, data-driven decision-making, and resilient digital infrastructure is where we need some talent. And that's where there's a huge gap. Growth is concentrated as it says here in specialist positions tied to AI, cloud, cybersecurity, and data. So, those are the core services and technologies that you need to understand. Now, one of the trends from this report from Talent 500 was around specialists earning the new premium. Essentially saying that companies are willing to pay more for specialists in very specific areas like AI, systems design, data infrastructure, security, and then domain-specific engineering like for example, cloud engineering. So, what's happening in the market today is there is explosive demand for AI infrastructure with cloud remaining the backbone for all digital systems. There's an ongoing shortage of skilled professionals and the gap is widening all the time. It's becoming a real problem for a lot of companies. There are rising salaries for specialized roles. Again, if you're just a generalist, it's not going to help you. You have to be a specialist. If you're a specialist, there's lots and lots of work available today and the salaries are very high. Now, the market hasn't changed as much as the criteria for hiring has. What people are experiencing today is that applying for jobs isn't working. Certifications alone are not enough to get a job and it kind of feels to a lot of people like the market has dried up. And this is totally in contrast to the data I was presenting before. There is a widening skills gap. As I mentioned, companies are finding it very challenging to find enough people to do the job. So, why is it that people are experiencing something very different? The reason is employers want people who can build real systems, solve practical problems, and show what they've actually done in the past. It's not harder today to get a job, it's just a lot more focused. Generalists are going to struggle, whereas specialists are the ones who are going to get hired. So, the reason that a lot of people are having a lot of difficulty is because they just don't have the right skill sets for the market today. Technology has evolved, we need to evolve with it. Now, there are two broad types of specialization I'm going to talk about

today. The first is role specialization. Essentially, this means choosing a career path and focusing your learning on the skills that you need for that specific role. So, companies don't hire just because you know cloud, they hire for very specific roles. For example, cloud engineering, security architecture or engineering, and cloud AI/ML engineering. So, they're hiring you for a very specific role, which means you need to make sure you have a specific skill set. You don't need to learn everything under the sun, that's the good news. What you need to do is focus your skills on these particular roles. What do I need to know to do these specific roles? So, as a cloud engineer or architect, you need to know how to design and build architectures using core services like EC2, auto scaling, load balancing, and VPC. If you're an engineer, then that's usually a more junior role, so you have a sort of narrower scope of responsibility in what you're designing, whereas an architect will be able to make much broader and more sort of draw on a lot more experience as they make more complex architectures. You'll need to know how to implement networking, subnets, routing, network address translation, security groups, work with storage services like block storage and file storage and object storage. Using infrastructure as code, so CloudFormation is an AWS tool for automating deployment of infrastructure. Terraform is a third-party tool that's used very broadly, so that's definitely a service that you should get to know and be competent with. And how to build CI/CD pipelines and monitor and troubleshoot using CloudWatch logs, metrics, etc. For a security engineer, again, being focused on AWS here, it's about how to design and manage IAM policies, roles, and least privilege access. Knowing how to secure AWS organizations using SCPs and setting up multi-account setups. Implementing network security, so another level of depth in the sort of more broader architecture around how you apply security to an organization at the network level as well as various other levels as well. Using different security services for detection, for analysis, for inspection. And enable logging and auditing and protecting data using various encryption technologies. As a Cloud AI/ML engineer on AWS, you'll be building and deploying models using tools like Amazon SageMaker, and you'll be using Bedrock as well. That's another common service used. Create data pipelines using a variety of services. Work with Python, very useful programming language to know in cloud as well as in AI. Um that will be used for data processing, model integration, and so on. And you'll need to know how to expose models via different APIs, serverless functions, and containers. And using managed AI services and integrating them into applications. So AWS already has a very large selection of AI services that they call machine learning services. Things like Recognition, Comprehend, Bedrock, and many, many more. And you'll need to know how to monitor and optimize models so for performance, for scalability, for cost. Now, the next type of

specialization is really like an evolution in terms of your experience from role specialization, and that's problem specialization. This means focusing on one specific type of problem you can solve for businesses. For example, maybe you're a security expert, so your thing is that you know how to properly secure AWS accounts, and you can take that out to market as an offering. Maybe cloud costs are too high, so you're going to go into companies and say, "Well, I know how to lower your cloud bill. " That's what I do. I go around companies, I work as a consultant. You could do this on a freelance model, for example, and you just go into companies and you help them to lower their cloud bill. And there's all sorts of other problems you can solve. Implementing AI capabilities, helping companies to build systems so they can scale properly, making sure that their systems are more reliable or their deployments are less error-prone and faster, monitoring and visibility like improving that for companies using a variety of tools. You can go in as the expert who knows exactly how to do that. Or managing access control and other security-related thing. Security is a big problem these days, and companies really need people who know exactly how to ensure that they don't have security breaches. So problem specialization is something that you can start working towards once you've gained more experience. I know people who specialize just in one of these areas. They work in a freelance capacity. Their LinkedIn profile, their resume, everything is aligned to I can help you achieve this outcome. And they get constant work, and you can get paid very well from this. You can now think of yourself a little bit more entrepreneurial because you can build up your own assets. processes and methodologies, and that gives you leverage and means that you can earn a lot more. Now, specialists earn a lot more than generalists. So just looking at some average salaries here, we can see where you have a specialization, where you're focused on something very specific and you have really deep technical knowledge in that area, the salary differences can be huge, and of course this compounds over the years. So what is the pathway to becoming a cloud

specialist? First, if you're starting out from scratch or you're coming from a tech background, you're just not a cloud person, you've got to make sure you learn some fundamentals, cloud basics, learn one cloud platform really, really well. You can always expand to other ones later on. Learn the core services. Everyone needs to know Linux and networking. If you come from a tech background, you might have that skill set already. Otherwise, you just got to learn it. So the goal is to understand how cloud systems work at a fundamental level. Next, we need to build practical skills. Hands-on labs and projects are the way to go. Deploy real applications that simulate real-world applications that companies would actually use. Know how to use different core services together. So here, we want to make sure that we're able to build and deploy solutions. Next, choose your specialization. Do you want to be a cloud engineer, an architect, a security engineer? Do you want to focus on artificial intelligence and machine learning or DevOps? So really focusing your learning and standing out. So once we have the fundamentals, we can say, "Right, now let's specialize. Now let's make sure that we align all of our training, our projects, everything that we're doing to build up our capabilities to the specific job role. We need to be able to prove to companies that we can do the job. As I mentioned, skills-based hiring means you need to prove that you're able to solve problems from day one. So you need to go deep and build a portfolio. You need to know advanced tools and patterns. Work on real-world scenarios. Get more complex all the time. Build and document projects. Show outcomes, so how you can actually help companies to lower their cost, increase performance, or increase their security. So developing high-value skills and proving your ability. Next, we position ourselves. You need to optimize your LinkedIn profile, your resume, your CV to make sure that it's aligned with the types of jobs that you're going for. Share projects and insights online. The more you are active and networking online and sharing what you're doing and what you're good at, the more likely people are going to come to you and ask for your help. Speak the language of business problems, not just technology. That's really important, especially if you're going to be a consultant. Lastly, we can learn to solve specific problems. This is where we're moving from role specialization, which helps us to get a job, so working as a cloud engineer or an architect or a security engineer. But now we're really specializing even further to that problem specialization space, where we're able to solve these specific problems for companies. Now, it doesn't mean that at this point you need to give up your job and go and freelance or consult. You can keep your job, but you're becoming a specialist in a very specific space. You can now become the thought leader who other people look to. And that can do wonders for your job. It can mean that you maybe you get promoted or you get moved into you move into another company where you can be that specialist, or you can go out into the world and start looking at other ways that you can really monetize that skill

set. Lastly, I'm going to cover my top five tips for standing out in 2026. So hopefully at this point in the video, you realize that I really believe that you need to specialize and not be a generalist. That's probably the most important thing this year, and I think it's going to become more and more true as the years go on. The highest demand sits at the intersection of cloud, AI, and data. Security is also very important. Now, if you focus on this, if you for example say, "I want to become a Cloud AI engineer. I'm going to focus on AWS, so I'm going to specialize there. I'm going to learn cloud engineering. artificial intelligence on AWS. " And that means that you're going to learn some data engineering because they are very related concepts, then that is a specialist skill set, and there's going to be a lot of work for you in the coming years. Now, everybody needs some AI skills regardless of role. So both from a productivity perspective and understanding how to integrate AI systems into applications, almost every role that you're going to work in the near future, if not now, requires you to know AI at some level. Portfolios do matter, but demonstrable hands-on skills matter more. So it's not just about showing off some fancy portfolio. We do need to create that. It gives us a lot of credibility. It's great for recruiters and hiring people to look at and say, "I can see what this person's done. " But we need to be able to pass those technical tests when we actually land those interviews. So having the hands-on skills is super important. And you got to optimize your resume, your LinkedIn profile to articulate the value that you bring to businesses. In a world of specialization, they're looking for that. In the world of skills-based hiring, they're looking for what exactly can you do for me? And if that aligns with the problems that they have as a business, they're going to call you out. They're going to ask you to come and help them out. Now, if any of you are

interested in working with me and my team, we do have a boot camp program. We have the Cloud Mastery boot camp. This is a program which includes live training, on-demand training, and project work, and lots of access to experts, including myself and my customer success team. You'll build in-demand specialist skills aligned with roles. It's all around AWS and adjacent technologies. So we'll help you to become an expert, a specialist in cloud engineering, cloud architecture, DevOps, cloud security, and cloud security architecture at a professional level as well. It's a really hands-on program, so you'll get very hands-on with the technology and build up a portfolio working on applications just like you would in the real world. And we really help you to build that portfolio, get noticed, and be ready for those interviews. If that interests you, please check out the link in the description of this video. I hope this was valuable, guys, and I'll look forward to seeing you in a future video. Please make sure you like and subscribe if you want to see more videos like this.