Hello Reader,

Gen AI hype is at an all-time high, and so is the confusion. What do you study, how do you think about it, and where are the most jobs? These are the burning questions in our minds. I love first-principles thinking, which means breaking down a problem into the smallest logical chunks, and I approach Gen AI the same way. Gen AI can be broken down into the following four layers:

• The bottom layer is the hardware layer, i.e., the silicon chips that can train the models. Example - AMD, NVIDIA
• Then comes the LLM models that get trained and run on the chips. Examples are Open AI, Anthropic etc.
• Then comes infrastructure providers who provide an easier way to consume, host, train, inference the models. Examples include AWS, Azure, and GCP. This layer consists of managed services such as Amazon Bedrock, which hosts pre-trained models, or provision VMs (Amazon EC2) where you can train your own LLM
• Finally, we have the application layer which uses those LLMs. Some examples are Adobe Firefly, LLM chatbots, agentic AI, agents with MCP servers etc.

Now, the important part - as you go from the bottom to the top, the learning curve gets easier, and so does the opportunity for new market players to enter. Building new chips requires billions of dollars of investments, and hence, it's harder for new players to enter the market. The most opportunities are in the top two layers. If you already know the cloud, then integrating Gen AI with your existing knowledge will increase your value immensely. If you are working in DevOps, learn MLOps; if you know K8s/Serverless, learn how you can integrate Gen AI with those; if you work in an application; integrate with managed LLM services to enhance functionality, you got the idea. I am focusing most of my time on this layer!

This brings me to the next point - is it possible to learn EVERYTHING on Gen AI?

Trying to master every aspect of GenAI is impossible and unnecessary. It's like asking a programmer to be an expert on every algorithm possible - a total waste of effort. You only need to go deep on areas that will get you the job, and then the ones your job uses.
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Use the T-shaped learning framework:

Breadth: Understand the fundamentals - how models work, their capabilities and limitations, how they’re trained, integrated, and used. Build a solid, high-level understanding across the field (think Level 100/200),
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Depth: Choose your niche, maybe you are already deep in Serverless, so learn more on integrating Gen AI with that. For example, running MCP servers on lambda, building agents using Serverless, event-driven LLM calling, Gen AI use cases with Serverless etc., and go deep (Level 400+).
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Question for you readers - Have you already started learning Gen AI? Are you thinking about it, or perhaps you think this whole thing is a farce and will blow over soon? Feel free to reply, and let me know!

Keep Learning and Keep Rocking 🚀,

Cloud With Raj

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Keep learning and keep rocking 🚀,

Raj