It's 2024 and a lot has happened in tech this past year. We've had hiring freezes and mass layoffs in 2023, and we've also had the rise of generative AI. It's a rapidly changing landscape for the industry right now, and in this video, I'm going to share with you what I think are the top 5 in-demand tech skills to learn for 2024. So if you want to move into a high paying career path, or you just want to get further ahead in your current role, then I think that these are going to be the themes that you want to pay attention to. And I'll show you all the data behind why I believe that as well. The criteria for these skills is that they have the potential to earn at least 120k USD in the United States, and that it also leverages current tech trends into 2024. But it's not too reliant on the trends either, so they have to be something that is future proof with long term demand. They also have to be skills that are in demand globally, as this will give you a lot more options when it comes to finding a job or leveling up in your roles. And if you disagree with any of these skills, or you think that I've missed something out, then please let me know in the comments because I'd love to hear what you think. Let's get started. Number 5 is generative AI, and this shouldn't come as a surprise to anyone. Whether you believe it's going to be the next industrial revolution or just an overhyped fad, generative AI has been the defining technology breakthrough of 2023. OpenAI has led the charge with ChatGPT for text generation, but now competition's heated up and every major tech giant has an AI offering to compete with it. Image generation has also taken off with Dall-E, MidJourney, and Stable Diffusion, all able to create stunning artwork just from a text-based prompt. Anyone who is quick enough to leverage these technologies and turn them into niche services, like this portrait generation app, Lenza, has already made millions of dollars in profit. But this is still just the beginning. AI is still continuing to improve rapidly, and investors are looking for profitable ways to apply them. Just look at the amount of funding being invested into generative AI startups this year. Here's a rough breakdown of how that money is being spent. The biggest portion is going into the development of AI assistants and interfaces. Social media and marketing content is the next biggest application, followed by photo and video editing tools. Now, I don't think most of these companies are going to be developing or training their own AI model. They are likely going to be just fine-tuning or building on top of the big foundation models and adding their own unique spin to it. So if you want to take advantage of this situation, I recommend learning how to use generative AI tools for both text and images via API. I don't mean you should become a machine learning engineer or an AI engineer. Nothing wrong with that either, but the demand for that role is going to be less than engineers who actually go on to apply generative AI. So that means becoming a software engineer that understands how to use these foundational models to achieve a business purpose. For text, there's OpenAI, AWS Bedrock, Llama2, and Google Gemini. Additionally, learning a library like Landchain will also help you to build more complex apps on top of these LLMs, and it will also let you swap around the LLMs that you actually want to use. It's also got a bunch of tools that let you work with your own custom data or APIs, so you can use it to build something like a company-specific chatbot or AI agent. For image generation AIs, I still think that MidJourney is currently providing the best-looking images right out of the box. It's not the easiest one to use, because I don't think that they have an API available yet, but I think that the quality of the images is still the best one, and this might be a subjective opinion. But if you want to be more productive with it, then Stable Diffusion is currently the one that gives you API access, and also the most customization. For example, with the use of control nets in Stable Diffusion, you can generate rich detailed images based on the shape of a scribble, like in this example here. So if you want to build products around image generation, then learning Stable Diffusion is probably still the way to go. So how much can you make with this skill? Well, most people working with generative AI will still have the title of either software engineer or something like data scientist or maybe even applied scientist. But as you can see from this data, jobs requiring the generative AI skill specifically has increased by 300% year on year. So even though you might not find a role like generative AI engineer specifically, this skill in particular is going to help you get more traditional roles like software engineer or applied scientist. The median salary for a software engineer in the United States is $170,000, and this is data from levels.fyi. And also to touch back on something that I mentioned before, although there are also well-paid roles for machine learning engineers, AI researchers, and AI engineers, most of the market is going to be for applying the AI models, not building or researching them. So you can think of this more as a specialization on top of regular software engineering, where you're learning a new skill or framework. Moving on to the next skill on the list, number four is productivity automation. This is more commonly known as robotic process automation, or RPA, but I think that the term productivity automation is actually a far better fit for describing what it actually is. IBM defines this field of work as using automation techniques to mimic back-office tasks of human workers, such as extracting data, filling in forms, moving files, etc. So in contrast to the previous point, which is about using generative AI to build products, this skill is more about leveraging existing products, AI or not, to boost the productivity of you and your team. This is a skill that's going to be useful for companies in every single industry. It basically pays for itself. For example, if you have a team of five people and you're able to use technology to help them each become just 20% more productive, then it's almost like you've hired an additional person into your team for free. Companies around the world are already beginning to use this, and with the introduction of generative AI, this trend is accelerating. Commonly automated tasks include things like creating draft documents, personalized marketing and summarizing meeting notes. So how do you take advantage of this? Well, you can start by first becoming familiar with the tools in your industry that help to automate the tasks that are boring or repetitive. There's a lot of these out there right now and they are always changing, so I can't list them all, but you should be able to find them with a quick Google search. Then you can learn how to apply them to increase productivity. You can either apply them to yourself, to your own organization, or even sell the service of implementing them to clients. So this would be like running a software consulting agency. For example, if you are a software developer, then think of tools like GitHub CoPilot. The research from GitHub shows that developers who use Copilot are more satisfied with their work and 96% faster with repetitive tasks. Anyone who knows how to use these tools well are going to have an edge over their competitors. And as a bonus, just being aware of these tools will also help you decide where to invest or not invest your time. You want to invest it into skills that the automation still hasn't been able to solve. Automation skills are also likely going to be mostly used within a software engineering role. So the same salary figure from before is relevant here. The next skill is going to be cybersecurity. Cybersecurity is about protecting your systems and data from digital attacks. It's a field that has always been well compensated since the very beginning, because it is something that no organization can really afford to compromise on. Recently, Rockstar's GTA 6 trailer was hacked and leaked as a result of a security vulnerability in their system. This incident was reported to have cost them $5 million and thousands of hours of staff time. Because the damages can be so severe, you can probably understand why cybersecurity is such a well-paid profession. As the AI boom is happening on the front page everywhere in 2023, the number of security-related incidents is also rapidly increasing. Here is data from CrowdStrike showing a 50% increase of intrusion campaigns year on year. This trend will also likely continue to accelerate. Banks and payment systems are becoming increasingly digital. Online spending is increasing, more workloads and data is being stored in the cloud, quantum computing is also developing rapidly, and soon on the verge of being able to crack RSA encryption. But I think the most interesting factor is going to be whether or not teams around the world start to cut corners as they rush to be the first to launch their generous AI product this next year. All of these things that I mentioned are going to create more and more openings for attackers. But at the same time, this also means that there's going to be an increased demand and opportunity for cybersecurity professionals. The median salary for a security analyst in the United States is $130,000 USD. Even if you don't fully specialize in computer security, I think that just spending time to learn about it and keep up to date about security events going on is going to go a long way in 2024 to set you up for success. The next skill on the list is cloud computing. The cloud computing market has a ton of momentum. It's already grown by 20% in 2023, and it's going to continue to grow at a similar rate in 2024 and beyond. Although this is already a $500 billion market, more than 50% of companies around the world are still running technology on premise, and they have not yet moved to the cloud. This means that there's still a lot of room to grow. Cloud computing is really popular for tech companies, especially startups, because it lets you keep your capital expenditure low. You can rent the hardware and services you need, and it's all secured and maintained for you, and you just pay for what you use. This will let you scale up and down extremely quickly, and it also lets you build and deploy your product faster. Now the catch here is that there is quite a steep learning curve, and each cloud provider will have different features and challenges. So what should you do if you want to learn cloud computing? Well, there's a lot of free courses and tutorials online for all the major cloud providers, such as AWS, Azure, and Google Cloud. All you have to do is just pick one of these and start learning, and all of these will also have a free tier to help you get started. If you don't have a preference or don't know which of the three to choose, then I recommend going with AWS, because it's still the most widely used cloud provider, which means that a lot of companies are also hiring for that skill. You don't need to spend any money on paid courses or certifications either. I mean, you can if you want to, but I don't think it's really that important. What's important is that you actually learn the skill and learn how to build apps on top of it. And this may be anecdotal, but I've never seen anyone get hired because of a certification, or rejected because they lacked a certification. It's really all about what you know and what you're able to build, and that usually gets found out during a conversation or an interview. Now, aside from learning how to use a specific cloud provider, there's also a lot of general cloud skills that are useful to learn as well. For example, these are things like how to design and build distributed systems, how to build token-based authentication, and how to build for ARM64, which is 50% more energy efficient than Intel processors. And if you haven't already, you should also learn how to use containers. So these are things like Docker images. This will let you move your code between platforms really easily, and that includes also how to build your code that can run on both Intel and ARM64. Learning cloud and distributed system skills are useful for a lot of roles, including software engineer. It's also an essential skill if you want to become a solution architect, which is sort of like designing how different pieces of the cloud will work together to solve a business problem for a client or for your company. The median salary for a solution architect in the US is $193,000. And finally, the last skill we're going to cover on this list is leadership and soft skills. Now, before you click away from this video, just give me a moment to explain, because I think this one is actually really important. If you already have strong technical skills and are already in a well-paid role, but you want to scale up even further to the next level, then I think it's going to be the soft skills and leadership skills that will help you advance, even in highly technical fields. This has always been true for a long time, but I think it's especially true in 2024, when there's a lot of talk about jobs being automated away, hiring freezes. You really need something special to stand out. Once you reach a certain point in your tech career, the only way to progress and increase your pay is to move into some kind of leadership role. That could be like a team manager role, a technical leader role, like a staff engineer, or even an executive role, like a founder or a CTO. The one thing that these roles all have in common is that emotional intelligence and soft skills are a stronger predictor of financial success, and not just by a little bit, but by quite a large margin of 85%. This data is from research that was carried out by the Carnegie Institute of Technology. It was more than 10 years ago, but I think it's more relevant now than ever, precisely because we're beginning to see generative AI able to automate away parts of the tedious but time-consuming technical work. Okay, but how do you take advantage of this? What skills should you invest into specifically? But to give you some ideas, here is data from a Bloomberg survey where they asked tech recruiters what skills were rare in candidates, but also highly desired by companies. The ones to pay attention to are in the top right corner, which are skills that are both rare and highly in demand. There's a couple here, including things like strategic thinking, leadership skills, but I think the most important one of all is communication skills. And as you can see on this graph, it's all the way to the right of the graph, which means that it is the most in-demand skill out of all the ones you see here. Developing communication skills means learning how to organize your thoughts, how to share complex ideas with simple words, and also how to ask the right questions. A good way to apply this is to do a lot of writing, and to also pay attention to the writing of other people who are good at communication. And you can be writing work logs, documentations, wikis, GitHub comments, whatever it takes. Because to write clearly, you also have to think clearly. In fact, here's a clip from Jeff Bezos from a recent podcast, where he's explaining why he prefers meetings with written memos rather than PowerPoint presentations. My perfect meeting starts with a crisp document. I like a crisp document and a messy meeting. You can hide a lot of sloppy thinking behind bullet points. When you have to write in complete sentences with narrative structure, it's really hard to hide sloppy thinking. It forces the author to be at their best. They're getting somebody's really their best thinking. And then you don't have to spend a lot of time trying to tease that thinking out of the person. You've got it from the very beginning, so it really saves you time in the long run. So if there was just one thing that you took away from this video, it should be that leadership skills, such as writing and communication, is going to serve you extremely well in any technical role. And especially if you want to advance into leadership roles. Leaders in tech can have a lot of different titles. But one of the most common roles is a software engineering manager. The median salary for this role is 300,000 USD in the United States. Other well-paid leadership positions include roles like principal engineer, staff engineer, executives and technical co-founders. There's still a lot of great skills and career options not mentioned on this list. Classic roles such as traditional software engineering, data science and UX design are still going really strong. They always have been and they still are. But it's just that this past year has created more openings for the skills that I covered in this video. And if you disagree with any of these or you think that I've missed something out, then please let me know in the comments, because I'd love to hear what you think about it. And if these skills are things you want to learn in 2024, then please let me know which specific topics you'd like me to cover more of right here on this channel. Thank you for watching, and see you next time.
