AI Bubble is about to BURST? What should software engineers do? Unfiltered Advice to You!

Summary

The video examines fears of an imminent AI bubble—highlighting a huge gap between recent AI capital expenditure and actual revenue, circular financing between cloud, chip, and AI firms, and overblown headline predictions. It debunks extremes (like 90% of code being written by AI), notes layoffs have many causes, and warns against social-media-fueled polarization. Practical guidance for software engineers: keep fundamentals (DSA, HLD) strong, learn to use AI wisely rather than blindly, and focus on building agentic GenAI applications with established frameworks. Bottom line: AI won’t fully replace engineers, but those who leverage it will outcompete those who ignore it.

Takeaways

• 💥 The AI investment landscape looks like a bubble: massive capital spending (hundreds of billions) is far outpacing current AI revenue.
• 🔢 Concrete numbers matter — huge gaps were cited (e.g., $560B capex vs $35B revenue; projections of $2T compute needs vs $200B revenue by 2030).
• 🔁 Circular/vendor financing is a red flag: big tech investments into AI startups create intertwined dependencies (Microsoft↔OpenAI, Amazon↔Anthropic, Nvidia↔OpenAI).
• 📉 Predictive headlines (a 2026 market crash, ‘‘AI crash’’) exist — but broader macro factors (economy, geopolitics) also influence outcomes.
• 🧾 Many high-profile CXO predictions and hype claims (like “90% of code will be written by AI in 6 months”) have not matched reality.
• 🧠 Social media and LLMs tend to validate what you want to hear — beware extreme opinions (AI is dead vs AI will replace everyone).
• 🔧 For software engineers, foundations still matter: DSA, system design (HLD/LLD), and core engineering skills remain essential to use AI effectively.
• 🤖 AI is an augmenting tool, not a magic replacement — engineers who adopt AI will outperform/replacе those who ignore it.
• 🧩 Using AI blindly is risky: generated code can hide errors; beginners who lack fundamentals will struggle to debug or integrate AI output.
• 🚀 Building agentic/GenAI apps is achievable quickly — pick a framework (LangGraph, SDKs) and focus on practical projects to gain confidence.
• 📚 Recommended learning path: basic Python libraries (NumPy, pandas, matplotlib), then LangGraph/SDKs, MCP/multi-agent patterns, and optionally LLM internals.
• 🧪 Hiring practises are evolving: interviewers accept AI use but expect candidates to understand and validate generated code — practical skill > blind copy-paste.
• 🎯 Career advice: don’t base your future on one influencer’s prediction — upskill in AI pragmatically while keeping strong engineering fundamentals.
• 💼 Practical tip: use company training/reimbursement where available to learn AI; many courses and free videos can get you started in a weekend.

Q & A

Why is it important for software engineers to learn AI according to the speaker?

The speaker emphasizes that software engineers who learn and use AI will increase their efficiency and productivity. These engineers will be more competitive, as AI can help them accomplish tasks faster and more effectively compared to those who ignore AI.

What is the role of AI in the future of software engineering?

AI is expected to become a norm in the software engineering field. Engineers who embrace AI will be able to enhance their work processes, automate tasks, and stay relevant in a competitive industry. Those who resist learning AI may become irrelevant in the near future.

What resources does the speaker recommend for learning AI and Python?

The speaker recommends free resources on their YouTube channel, including videos on key Python libraries like Numpy, Pandas, and Matplotlib. They also suggest going through Langraph for learning about AI frameworks and MCP for understanding multi-agent architecture.

How does the speaker suggest beginners approach learning AI?

For beginners, the speaker recommends starting with basic Python libraries like Numpy, Pandas, and Matplotlib. They suggest watching free videos on these topics and gradually progressing to more complex AI frameworks like Langraph and MCP.

What is Langraph, and why is it recommended for beginners?

Langraph is an established AI ecosystem that the speaker recommends for beginners. It is one of the oldest and most trusted frameworks in the industry, making it a good starting point for those new to Python and AI.

What does the speaker mean by 'AI will not replace software engineers, but engineers who use AI will replace those who do not'?

The speaker is conveying that while AI will not replace software engineers entirely, engineers who use AI to improve their work efficiency and productivity will outperform those who do not adopt AI, making them more valuable in the industry.

Why does the speaker stress the importance of upskilling in the tech industry?

The speaker stresses that upskilling, especially with AI, is essential for staying competitive in the fast-evolving tech industry. History has shown that engineers who learn new technologies, like AWS in the past, remain relevant, and the same will apply to AI.

How does the speaker advise engineers to stay relevant in the job market?

To stay relevant, the speaker advises engineers to keep up with the latest industry trends, especially AI. This includes continuously learning and upskilling to avoid being left behind in a competitive job market.

What are the potential benefits of enrolling in the speaker’s boot camps or online courses?

Enrolling in the speaker's boot camps or online courses offers lifetime access to course recordings, ongoing doubt resolution through Discord, and practical experience with AI frameworks. These courses are also eligible for company reimbursement in many cases.

How does the speaker suggest engineers take advantage of company reimbursements for AI courses?

The speaker encourages engineers to check if their company offers reimbursement for courses and suggests using this benefit to enroll in AI-related courses. Many companies provide this reimbursement, making it a cost-effective way to gain new skills.