Future of AI & Software Engineering - My Honest Opinion - Bubble vs Reality!

Summary of the video’s main points

• Context & motivation: The speaker reacts to a long, highly negative 3-hour video by Akshay Saini about the future of AI and software engineering. The speaker argues that Akshay’s video went viral in a pessimistic way, leaving current IT workers and job-seekers feeling “depressed” and hopeless.

• Agreement with Akshay (AI capability): The speaker concedes that AI has become genuinely capable, especially for coding. He claims AI tools/models can now perform well on real, large projects and that, in the future, companies may write less code (or rely less on human-written code), making these tools potentially cost-effective.

• Disagreement (cherry-picked “evidence”): The speaker criticizes Akshay’s approach as selective and sensational, arguing that Akshay picks a few negative stories from around the world to conclude that the market is “ending.”

  • Claim 1: A “₹40 lakh package → job loss → Rapido driver” story is fake. The speaker argues the post/article lacks credible sourcing and was likely made to attract views. He also points out that the logic is questionable—for example, it doesn’t make sense that someone with a high-value package would quickly switch to low-paid driving just to handle expenses.

  • Claim 2: A “25 years at Adobe → school bus driver” example is reframed as context rather than failure. The speaker argues the individual had a long career and was likely near retirement age, suggesting the person may have chosen or transitioned out of work rather than being “forced” by industry collapse.

  • Claim 3: Layoff statistics are overstated if you ignore scale. He challenges the implied conclusion that “jobs are disappearing” by doing a rough calculation:

    • A layoff tracker shows about 1,300 layoffs (in 2026) for software developers/coders
    • Compared against ~47 million developers (using 2025 numbers)
    • Result: ~0.28%, meaning the vast majority (~99.7%+) are still working He adds that layoffs appear broader than just coding roles.

• Counter-evidence (still hiring): To balance the narrative, the speaker highlights positive hiring signals, such as:

  • Campus hiring / internships and placement reports (e.g., Accenture hiring students; NIT Durgapur placement figures with high CTC offers).
  • He argues that if the industry were truly collapsing, large companies and institutes wouldn’t be hiring as actively as reported.

• Main worldview: transition, not termination The speaker explicitly says layoffs are real, but argues people shouldn’t simply quit. He frames AI as a disruptive shift that changes job types rather than eliminating work entirely. He compares this to other life disruptions (accidents, divorce, etc.) to argue that negative events don’t stop people from continuing to live—so workers shouldn’t give up on the industry.

• Why older coding skills feel “zero” (but isn’t the end): He argues developers who relied heavily on manual coding skills may feel obsolete because AI is the next “level” of coding. However, he believes this will evolve into new responsibilities beyond raw coding.

• Critique of course-sellers/content creators: The speaker argues some people left jobs to sell courses when coding demand was high. Now, as the value of coding changes, they feel threatened. He suggests this fear can spread pessimism and distort the “everything is ending” narrative.

• What new jobs may look like: AI supervision and orchestration He argues that coding may become less about writing syntax and more about:

  • orchestrating AI agents
  • managing workflows/data pipelines
  • verification / validation
  • adding guardrails
  • handling training/deployment
  • AI risk management He also gives an example of an AI camera misinterpreting a guitar as a person without a helmet, to show that AI systems can fail and therefore need human oversight.

• Learning advice: learn AI + software engineering, and focus on system design The speaker recommends aspiring professionals:

  • learn software engineering fundamentals
  • then learn AI
  • prioritize system design over chasing coding syntax He argues that doing this now is safer because future job roles are often hard to predict when a new technology first emerges.

• Optimistic conclusion: The speaker argues the future workforce will adapt, new roles will emerge, and people who keep learning will benefit. He also claims that if some fearful people leave, it could improve the industry’s reputation by pushing out those unwilling to adapt.

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Presenters / contributors mentioned

• Akshay Saini (creator of the video being responded to)
• The speaker (unnamed) (author of the response video)