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

Overview

The speaker argues that the AI/software-engineering world is in a painful transition. Layoffs and hiring slowdowns are happening across many sectors, while AI is rapidly becoming capable of large parts of software engineering—especially coding support, debugging, testing, and code review. However, AI does not automatically “end” software jobs. Instead, the market shrinks in many places, roles shift, and engineers must adapt quickly.

1) “Reality vs bubble”: layoffs are not only an “AI excuse”

• The video claims the fear that “AI causes layoffs” is often understated publicly by companies.
• Layoffs are typically framed as reorgs, leadership changes, or project changes rather than direct AI-driven cuts.
• The speaker supports this with examples of people losing high-paying tech jobs and being pushed into non-tech work, including layoffs at major tech companies and even in banking.
• A key theme is that layoffs have continued well after COVID, making the “AI bubble” narrative misleading.
• The speaker also cites online “layoff trackers” and charts showing ongoing quarter-by-quarter layoff activity, including “AI-attributed” layoffs rising after the COVID hiring boom.

2) Companies are shifting business models (example: TCS)

• The speaker argues large IT firms are not sticking with the old “human labor outsourcing” model.
• They claim firms like TCS are moving toward GCC setups and AI/data-center/agent-driven automation.
• This is framed as a survival strategy, even if it conflicts with the firm’s traditional approach.
• Stock performance is referenced as support for the idea that even large firms are under pressure and must change.

3) AI capability in software engineering is improving fast (and is already usable)

The speaker emphasizes that modern models can handle much of the engineering workflow, including:

• Code generation
• Debugging support
• Code review assistance
• Writing test cases (including regression and corner-case coverage)
• Deployment-related tasks “to a large extent”

They also argue that earlier skepticism (“AI can’t write good code”) is outdated, especially due to major improvements like larger context windows.

• A specific model family is mentioned as being used personally (pro-plan usage), as evidence that AI can produce practical, reviewable outputs rather than only “toy” code.

4) Why companies might not be “profiting” yet doesn’t mean AI will stop

• The video addresses claims that AI companies (e.g., OpenAI/Anthropic) aren’t profitable.
• The speaker’s counterpoint: profitability isn’t required immediately because funding, IPO paths, and investor demand can continue.
• They argue that large sums of capital are still flowing into AI globally—across semiconductors, Big Tech, and cloud/AI infrastructure—making it unlikely the trend reverses soon.

5) Core forecast: software careers will be harder; jobs won’t vanish overnight, but hiring will shrink

• The speaker’s POV is that software engineering hiring will decrease and layoffs are likely to continue.
• New entrant hiring (new grads, junior roles) is described as especially difficult—the market can’t absorb new hires at the current pace.
• “AI engineer” roles may not fully offset losses because:
  • AI often changes existing jobs rather than creating net-new ones at the same scale.
  • Existing engineers can upskill into AI-augmented workflows, meaning displacement happens internally.

6) Education/training: DSA is still being asked, so upskilling must include AI

• The speaker criticizes the idea that DSA should remain the dominant interview filter, but argues it still happens in many places.
• Advice includes:
  • Learn AI tools and use them in daily engineering work
  • Build projects (especially for startups)
  • Avoid over-investing in paid courses if money is limited—use free resources first
  • Stay adaptable as interview processes slowly change

7) Practical advice to different groups

Senior engineers (years of experience)

• Don’t become rigid; upskill with AI and broaden responsibilities.
• Expect possible role changes—flexibility is positioned as job security.

Junior/new engineers and freshers

• Use AI immediately to improve output and productivity.
• Get referrals; application volume is high and filtering is harder.
• Keep initial salary expectations lower, build experience/value, and stay “ready” for interviews.

College students

• The speaker discourages spending 4 years on CS/engineering “just because,” arguing it may be less efficient than building skills directly and using AI-assisted projects.
• They also claim colleges often don’t teach effectively enough, increasing student pressure and demotivation.

8) Mental health and risk management

• The video warns against doom-scrolling and excessive panic.
• It recommends:
  • Physical exercise (walks/gym)
  • Talking with family/friends
  • Being conservative financially (avoid large liabilities/major decisions during turbulence)
  • Keeping hope while accepting uncertainty

9) Defending themselves against “course selling” allegations

• The speaker says they are not trying to scam or profit from fear despite running courses.
• They advise viewers not to take courses if they don’t find value.
• They frame their education content as a response to ongoing interview requirements and real market transitions.

10) “AI will change roles” (toward AI full-stack / end-to-end responsibility)

The speaker suggests future engineers will cover more of the lifecycle with AI assistance:

• Ideation → requirements/docs
• Architecture
• Development
• Debugging/testing
• Deployment
• Maintenance/security

They also claim AI can cover much of QA work, pushing engineers toward broader capabilities rather than staying in narrow roles.

Presenters/Contributors

• Akshay (the sole presenter; also referenced in chat as “Akshay” / “Akshay Taneja”)