High Paying Technologies I am learning in 2025

Summary

In this video, the speaker delves into the complexities of modern web development and the potential of AI. They discuss the challenges of building computationally intense applications in browsers, such as video editing and image generation, and the need for web workers to handle heavy tasks efficiently. The speaker also emphasizes the importance of mastering cutting-edge technologies like machine learning, LLMs, and diffusion models, which are revolutionizing industries. A proposed study group aims to dive deeper into these topics, offering a collaborative approach to learning the skills necessary for success in AI and advanced web development.

Takeaways

• 😀 **Web Workers are crucial** for optimizing computationally heavy tasks in the browser, such as video editing, background removal, and processing large spreadsheets.
• 😀 **Browser performance** can be significantly enhanced by offloading tasks to Web Workers, freeing up the main thread for other operations.
• 😀 **Designing computationally intense front ends** requires knowledge of how to optimize heavy tasks like video rendering and managing large datasets without blocking the browser's UI thread.
• 😀 **Generative AI for Image and Video**: AI-driven image generation (like Stable Diffusion) is rapidly evolving and has numerous applications in creative fields, such as creating video thumbnails or stock footage.
• 😀 **Video generation** is an emerging frontier, and mastering AI-based tools for generating videos and images presents huge opportunities in the content creation industry.
• 😀 **Understanding Diffusion Models** like Stable Diffusion is essential for those interested in AI-driven image generation, as these models are key to how AI creates images from prompts.
• 😀 **Machine Learning (ML) and Large Language Models (LLMs)** are highly valuable and in-demand skills, with transformer architectures leading the way in AI development.
• 😀 **Learning ML/AI without formal education** (such as a master's or PhD) is possible by finding junior roles or learning from the research community and peer networks.
• 😀 **The future of ML/AI** will likely be dominated by a few companies excelling in this space, making expertise in transformers and LLMs essential for those pursuing long-term careers in AI.
• 😀 **Creating a study group** to learn advanced topics like image generation, AI agents, and developer tools is a great way to build a knowledge-sharing community, despite potential challenges in maintaining motivation.
• 😀 **Priority learning areas**: Image and video generation, AI agents, developer tools, browser automation, core machine learning, and performance optimization are key focus areas for those looking to advance their tech skills.

Q & A

What is the main challenge in front-end development that AI cannot easily replicate?

One of the main challenges is optimizing computationally heavy tasks, such as video editing or handling large spreadsheets, directly in the browser. Web workers are often used to offload tasks to keep the main thread responsive, which is a complex skill for front-end developers.

What role do web workers play in web development, particularly in video processing?

Web workers help offload computationally intensive tasks, such as video processing, from the main browser thread. This allows the main thread to remain responsive while the heavy lifting is done in the background, which is crucial for tasks like video background removal or rendering large files.

Why is video and image generation an important area to learn in AI, according to the speaker?

Video and image generation, especially through diffusion models, are becoming critical areas in AI with wide applications. The speaker mentions that these technologies are not just useful for entertainment but can also disrupt industries like stock footage, content creation, and even more complex applications in virtual models and video generation.

What are some of the core technologies in machine learning (ML) that the speaker suggests learning?

The speaker suggests learning about neural networks, transformers, and large language models (LLMs). These technologies are key to understanding modern AI systems and have applications across various domains, from natural language processing to creative content generation.

What is the significance of transformers in AI, and how are they used?

Transformers are a type of deep learning architecture that are especially important in natural language processing (NLP). They form the basis for many advanced models like GPT, BERT, and other large language models (LLMs). Their ability to process large sequences of data in parallel makes them highly efficient for tasks like language translation and text generation.

What advice does the speaker give for those wanting to break into AI and machine learning?

The speaker recommends gaining foundational knowledge in machine learning, specifically by focusing on deep learning and transformers. While a master's degree or PhD is ideal for deep specialization, starting with online resources, research papers, and community support can also be effective. Practical experience, such as internships or junior roles, is essential for breaking into the field.

Why does the speaker believe that AI and machine learning will continue to be valuable skills in the long term?

The speaker highlights that AI and machine learning are fundamentally disrupting many industries, with high investment and rapid development. As AI becomes more integrated into everyday technology, skills in these areas will continue to be in high demand, with significant financial rewards for those who master them.

What specific areas of AI does the speaker find most interesting and why?

The speaker finds image and video generation, as well as AI agents, the most interesting. These areas are seen as having both significant technological potential and real-world use cases, from automating tasks to transforming industries like entertainment and content creation.

How does the speaker propose to structure a study group to learn about emerging technologies?

The speaker suggests forming a study group where members pick different topics related to AI, machine learning, and web development to study each month. The group would focus on silent learning, with members discussing and sharing insights on the chosen topics. However, the speaker also acknowledges that motivation may be challenging over time.

What are the key differences between image generation models like Stable Diffusion and other diffusion models?

While the speaker briefly mentions Stable Diffusion, the main point is the exploration of how different diffusion models generate images based on prompts. The differences between models likely lie in their architecture and the quality of output, but detailed understanding of each model would require studying their respective research papers.