0:00 You may have heard people say that cloud computing jobs are dead and artificial intelligence is just a massive bubble that's about to burst. So, in this video, I'm going to explore these concerns and I'm going to give you some of the facts about what's really happening in the industry, what's happening in terms of investment in data centers, the growth of the cloud computing market, and how artificial intelligence is evolving and maturing, and what that means for jobs. And I'm going to give you some tips. So, at the end of the video, make sure you stick around because I'll give you my five tips for what you can do to actually excel in the cloud industry and artificial intelligence in 2026. In 2025, we saw a huge amount of investment in data centers and artificial intelligence. So, you've probably heard about a lot of this in the news and it's continuing this year and way into the
0:46 future. So, back last year in 2025, the tech spending on AI reached 427 billion. That's tech companies investing in artificial intelligence. The global cloud computing market was about $912 billion and it's going to be expected to be around $1.1 trillion in 2026. So $190 billion increase in size. That's pretty big. Now a few examples of where we've had a lot of investment in this space. We saw project Stargate where $500 billion is being invested in AI infrastructure in the US. That means lots and lots of data center buildouts. Big tech are investing an aggregate of over 400 billion this year in 2026. So all of the big tech companies know that
1:33 AI is the future. They know that they have to make sure they innovate and expand into the space. So they're spending a lot of money on that. Amazon plans to invest over 125 billion on AI and AWS infrastructure. I've got a few more facts about AWS because they really understand what's going on here and they're investing and making a return on their investments very very quickly. AWS and OpenAI also announced a multi-year strategic partnership worth 38 billion and there'll be more news coming about that partnership soon I believe with Chat GPT expanding into the AWS platform. The hyperscala platforms like AWS, Microsoft and Google as well as Meta are expected to represent about 50% of the global data center capex. So that's capital expenditure by 2026. So
2:19 we're seeing a lot of investment in this space. And specifically on AWS, AWS posted its biggest growth surge in 3 years with 20% yearon-year revenue gains. And here another article saying that AWS is growing at a pace that they haven't seen since 2022. That's from the CEO Andy Jasse. And another quote here from Andy Jasse. He said, "You're going to see us continue to be very aggressive investing in capacity because we see the demand. As fast as we're adding capacity right now, we're monetizing it." This is super important for anyone who's concerned about what's happening in the industry. What you see here is the platform. So AWS being the platform on which many other artificial intelligence companies whether it's open AI or
3:07 whether it's a variety of different platforms SAS platforms and more who want to build AI enabled applications or run LLM models they need somewhere to do it and they do it on AWS. So from AWS's perspective it's just money coming in the door. They build out more infrastructure these companies invest. Now whether those companies are actually making money and have a long-term future is another matter completely. But AWS themselves are the ones who are making the money because they have the platform all of these companies are using and leveraging to build their AI capabilities. Now it's important to understand the Gartner hype cycle for artificial intelligence. Now we don't have the version of this for 2026 yet but I want to show you this one from 2025. So what we have with the hype
3:53 cycle is we start from an innovation trigger and what that means is there's some new technology and the technology will then go through a maturity phase and an adoption phase as well. And so what happens here where we have this stage of the whole life cycle that's called peak of inflated expectations. In the peak of inflated expectations, companies believe that technology is going to solve problems for them and they race to actually start deploying applications, building stuff and using this technology. Unfortunately, the reality is not what they expected it to be. It doesn't quite deliver the capabilities that they thought it would. Then what happens is the technology typically goes into the trough of disillusionment. So now what this means is people who should be the users of
4:38 this technology are a little disillusioned. They realize it's not quite mature enough to solve every problem to do exactly what they thought it was going to do yet and it needs to mature. So as it matures, it goes up this slope of enlightenment. Eventually expectations and reality meet together and you have a technology that can be used in production in the real world where companies can actually get a return on the investment that they're putting in and actually helps them to grow their businesses or deliver some kind of capability. So let's have a look at what we have here. We got generative AI starting to come down into that trough of disillusionment. So generative AI like chat GPT etc. that's mostly what we're doing these days with AI. So most people who know AI unless you're actually working in the industry will know it through generative AI. So
5:24 whether it's uh chat GBT or whether they're uh writing code using clawed code or GitHub code etc or GitHub code pilot rather we're using an LLM. So we're giving it prompts. we're asking it to do something, write answer questions, write an article, return some code to us, etc. Now, that technology is starting to come down into the trough of disillusionment. And I believe that when we have the hype cycle for 2026, it's probably going to be down here in the very bottom of the trough of disillusionment, maybe starting to come up towards the slope of enlightenment. We've then got AI agents. So, last year was all about agentic AI. Everyone thought they could build a aentic workflow. It was going to solve all their business process challenges. is they could you know get rid of people and the AI would actually take over the jobs and that's not exactly what
6:11 happened. So I believe that will be sort of coming down probably where generative AI is on this particular version of the hype cycle. Now what's really interesting what you see on the right hand side here is cloud AI services. So cloud AI services last year were already in the slope of enlightenment and they're going to be continuing their maturity through this year as well. So as I said before, what that means is that reality and expectations kind of come together for cloud AI services, meaning that the services that are offered by cloud companies like Amazon Web Services are already mature enough for companies to start utilizing them. Now many of the AI services on AWS have actually been around for many years, okay? Long before people started messing around with LLMs and chat GPT and all
6:57 that stuff. So we do see that companies can start building AI enabled applications today and get results using those AI services offered by the likes of Amazon Web Services as well as Microsoft Azure and Google Cloud Platform as well. That's why there is opportunity in this space because this is where people can already start building applications in production. Now there are a few misconceptions about the job market that I just want to clarify here. So, a lot of people believe that AI caused massive layoffs, that AI is starting to automate away people's jobs. It's just not the case yet. Most job losses over the last few years, right up to the present day, have been caused by the aggressive overhiring and bad
7:44 business decisions in terms of recruitment that actually happened back around the pandemic time, sort of 2020 through about 2022. So, at that time, money was very cheap. companies actually started really sort of hiring and trying to grow their businesses as quickly as they could while the cost of capital was so low. They overhired in a big way and ever since around sort of 2023ish a lot of layoffs have happened mainly for that reason. So what we see is a discrepancy between the jobs that actually are affected by this. So I'll have a look at which jobs are most affected and which are least affected in a moment. Now, many people think that there's a lack of job opportunities today, but it's just not true. There's actually a lot of opportunities for those who have the right skills. Hiring is a lot more
8:30 targeted now. Back in 2021, just having a certification might have been enough and companies were just hiring like crazy. Now, they're a lot more discerning and they really want to make sure you have the right skills and experience in order to do the job. Some people believe that AI is affecting job roles equally and that's just not true. So some roles are shrinking in numbers but others are seeing significant growth. So you've really got to look at where the opportunities are. So which roles get cut? Customer support, help desk specialists, roles that we can actually use either outsourcing to other parts of the world where labor is cheaper or perhaps we're actually able to use an AI chatbot and other types of automation, back office operations, payroll, admin support. Again, lots of
9:16 automated tasks. AI agents are going to be really useful there as they mature as well. Now content generation, so things like technical writing, you don't need as many people anymore because one person can do the job at 10. Graphic designers and UI designers, we've got a lot of low code, no code tools, uh really helping people to do this themselves and they don't necessarily have to actually go and hire someone to do the job. And entry-level programming doing routine coding tasks. So actually AI is really good today with coding. you know, it's not quite as good in many cases as the top level coders, but it's really close and it's going to exceed their capabilities in the near future. So, what we find is those who have uh more experience in the coding world can actually be the ones who are working with AI to generate code and they can be
10:03 far more productive than they would be if they did not have AI. Now, the roles that don't get cut are roles like cloud engineers. So, cloud engineers are building out the AI infrastructure. They're building out cloud applications. They're deploying systems. They're uh working on networking. They're working on compute. They're working on storage. They're working with infrastructure as code tools. They're doing a bit of design work. They're helping to deploy applications and infrastructure in the cloud. DevOps engineers working on DevOps pipelines. There definitely lots of scope for DevOps engineers today. And architects. So, solutions architects, they work with different stakeholders in a business. You can't just automate those jobs away because they have to talk to different people, find out how the business is doing and what technology problems they actually have
10:49 and what business problems they have that they can solve through technology. So, it's a great role that has a great future. And of course, those are actually working directly in AI and machine learning. So, the AI and the ML engineers, variety of different job roles associated with that technology. And all of those are in a big growth phase and they're all seeing a lot of uh really strong wage growth in that area. And of course data engineers and data architects. You can't have machine learning and artificial intelligence without data engineering and data architecture. So it all fits together. These are the job roles that are just not getting cut. When you actually look at any of the announcements, if you find the details of who actually got laid off by some of the big companies, you will very rarely see many people in this space. Okay, these are the areas where
11:34 they're actually still hiring cyber security as well. We obviously have to secure everything that we deploy. So this is where the growth is. If you can get these skills, these are the skills that are going to help you to make sure that you have a solid future in the industry. Now we see some really great salaries as well. So cloud engineers, solutions architects, machine learning engineers, really good salaries. And I mentioned here that the that the actual salaries are increasing. And that's because whenever I go back in every couple of months and I have a look in Indeed to see what the average wages are, it's always a little bit higher. So 2 3 4 5,000 higher. So that's quite a big growth if you think about that over the year. You know, every two or three months we're seeing a few thousand added to some of these roles. So really strong growth. And the strong growth is of
12:19 course because there aren't quite enough people with these skills to meet the demand that companies have. So, we're actually at a point today where companies need to hire the suitably qualified people for these roles and they can't find the right people. That's an opportunity for you. So, I'll give you my top tips for standing out in 2026. So, the first one is you need to develop specialist, not generalist skills. So, make sure you go deep first. You can go broad later in your career, but to get hired in the first place, you need to be really good at something. Now, what I say is that there's a few areas here where you need to be strong. You need to be strong in one cloud platform. Now, you can't go deep on two free cloud platforms unless you want to put in at least 12, 18 months or more
13:06 just learning the cloud, not the other skills which I'm going to mention you need. That doesn't make any sense. it's not even necessarily going to put you in a better position, meaning that you can apply for more roles because what really matters is being good at something, being focused and aligning your resume to that particular job role. So, what I say is get really good at one cloud platform first. So, choose which one you want to do. I come from a Microsoft background and yet I chose AWS as the platform that I became good at and started teaching just because it's the biggest platform. It's the most mature. That's true today just as it was several years ago and that's why I see more opportunities there. On top of that, you want Linux skills. Linux is used across the cloud. It's the most common operating system in the cloud. You'll want to have some infrastructure as code tools like Terraform. So have some skills in that space. You want to
13:52 understand networking at a good level as well. So those are a few core skills that you must have working in the cloud. Now, the highest demand sits at the intersection between cloud AI and data engineering. So, if you want to get really specialized and have a skill set that's in the most demand, having a strong cloud platform skill set, having some artificial intelligence skills and then some data engineering skills is going to put you in an amazing position. So, have a think about that on which uh skills, which certifications you might want to do in order to get those uh those skills. Now for example in the AWS space you can do the solutions architect for cloud you can do AI practitioner to get you the AI skills and data engineer
14:38 associate to get the data engineering skills and then for going a bit deeper you can go into machine learning as well. Now everyone needs AI skills regardless of the role that you're going to perform. There's two facets to this. So one is the ability to utilize AI to make yourself more productive. That's really important. So knowing how to do prompt engineering, knowing how to use tools for uh for getting answers to questions, helping you make design decisions, generating code, that sort of thing. And then there's understanding how to actually integrate AI uh capabilities into applications as well. That's where you need to know some of the AI uh the cloud AI services on platforms like AWS, for example. Portfolios matter, but demonstrable skills matter much much more. So everyone knows that you need to have a
15:24 portfolio so that you can get that job. It shows that you have some hands-on skills, but sometimes people focus too much on, you know, having lots of different portfolio applications and they think that's going to land them the job. It's useful. It's necessary. It's not the only thing. You must be able to actually sit in an interview and if they put a challenge in front of you or ask you to describe how something works that you're able to answer those questions or actually deploy or fix a problem. So, having the hands-on skills is super super important. And you need to optimize your resume for LinkedIn to articulate the value you bring to businesses. So make it very clear these are the problems I can solve for you based on the skills that I have and you want to align that very closely to the type of job role that you want to apply
16:10 for. Don't apply for everything. Apply for what you're good at. So look for the job role that you're good at that you focused your skills and your training on. And then you can optimize everything for that particular job ro. when the right job comes along, you're going to be you're going to stand out really well. So, those are my tips for standing out and getting a job in 2026. And if you [clears throat] want to work with me and my team, of course, we have our cloud mastery boot camp program where you can work handson building skills live sessions as well as lots of skills assessments and capstones and various different project work. So, you can really develop that hands-on skill set and get certified and have the practical skills that you can actually demonstrate to employers as well. If that's something that interests you, check the description of this video and you'll find some more information about how you
16:56 can apply. [music]
