What AI Is Forcing Every Founder to Rethink (feat. Suri Nagarajan)
Lifelong learning isn’t optional in software anymore—AI has simply made that reality impossible to ignore. Only those who stay curious, invest in depth, and adapt continuously will thrive.
In this engaging conversation, Krish Palaniappan and Suri Nagarajan delve into the intricacies of investing, particularly in the tech sector versus non-tech industries. They discuss the importance of financial literacy, the risks associated with high-risk investments, and the significance of diversification for new investors. Suri shares insights on industry allocation for a hypothetical $100 investment, emphasizing stable sectors like healthcare and energy. The discussion also touches on the cyclical nature of markets, the risks of entrepreneurship versus employment, and personal reflections on career choices and cultural opportunities in the US. Ultimately, the conversation highlights the importance of pursuing one’s passions and the unique educational experiences available in America.
The Future Belongs to the Continuously Curious.
Podcast
Summary
Learning Never Stops: AI, Education, and the Changing Shape of Software Teams
In an industry that reinvents itself every few years, one truth has remained constant: the people who thrive are the ones who keep learning. As artificial intelligence accelerates the pace of change across software engineering, data, and product development, the definition of “learning” itself is being rewritten.
In a recent conversation, we explored what lifelong learning looks like in practice—through the lens of returning to college after two decades, embracing AI-driven disruption, and rethinking how modern software teams are built.
This is not a story about credentials. It’s a story about curiosity, adaptability, and staying relevant in a world that refuses to stand still.
Returning to School After 20 Years: Why Bother?
Going back to school after nearly two decades is not an obvious choice—especially for someone already leading global AI and analytics teams in financial services. Yet that decision reflects a deeper motivation: understanding why things work, not just how to make them work.
Modern AI tooling makes it easy to build models. Tutorials, YouTube videos, and frameworks can help anyone get something running. But there’s a difference between using a model and truly understanding it. That difference lives in mathematics, theory, and rigor—areas that are difficult to master without structured education.
Formal programs provide more than content. They provide:
Access to professors who live and breathe the subject
Peer discussions that challenge assumptions
Feedback loops that force clarity of thought
For those who crave depth, not just speed, education still matters—especially when chosen intentionally.
Is College Still Worth It?
This question has no universal answer.
College in the U.S. has become expensive, and student debt is a real burden. Many roles—especially in software—no longer require a degree. Some companies even hire directly from high school or alternative training programs.
Yet dismissing college entirely misses the point.
The real value of college isn’t the diploma. It’s exposure:
Exposure to ideas outside your comfort zone
Exposure to industries you may not have considered
Exposure to people who think differently than you
When education is treated as a default checkbox, it often disappoints. When it’s treated as a deliberate investment in depth and perspective, it can be transformative.
Continuous Learning Is Not Optional in Software
Software engineering has never been static. JavaScript frameworks change constantly. Infrastructure paradigms shift. Tooling evolves.
AI has simply compressed the timeline.
What used to change over five years now changes in one. The engineers who succeed are not those who memorize tools—but those who develop the habit of learning continuously and applying what they learn quickly.
Learning today happens everywhere:
In classrooms
On the job
Through experimentation
Via failure
The source matters less than the mindset.
What Actually Motivates Lifelong Learners?
Not everyone is wired the same way.
Some people want clear instructions, predictable work, and a stable paycheck. There’s nothing wrong with that.
Others are driven by something else: the urge to build, to improve, to see what’s possible next. For this group, motivation comes from:
Challenging themselves
Solving harder problems
Staying on the leading edge rather than the crowded middle
Money, titles, and recognition follow—but they aren’t the primary fuel. The real reward is the satisfaction of mastering something difficult.
Fear vs Curiosity in the Face of Change
Every major technological shift creates anxiety. AI is no different.
Some people see disruption and freeze. Others see disruption and lean in.
History suggests that change doesn’t eliminate opportunity—it reshapes it. Entire roles disappear, but new ones emerge. Organizations fall, others rise. The constant is not technology, but adaptation.
The most resilient professionals treat change as a challenge to conquer, not a threat to avoid. They ask:
How does this work?
How can I use it?
How do I stay ahead of it?
That mindset turns uncertainty into momentum.
How AI Is Reshaping Software Teams
Traditional software teams were built around specialization:
Developers wrote code
Testers tested
DevOps handled infrastructure
Project managers coordinated work
AI is blurring these boundaries.
Testing, infrastructure automation, and even documentation are increasingly handled by AI-assisted tools. Developers are becoming more self-sufficient—able to write code, generate tests, and deploy systems with far fewer handoffs.
The future team looks leaner:
Fewer specialized roles
More capable developers
Strong product and domain understanding
AI agents acting as force multipliers, not replacements
AI doesn’t eliminate developers. It amplifies the best ones.
Will We Need Fewer Developers?
If the industry did only what it does today, the answer would be yes. Productivity gains mean fewer people can deliver the same output.
But that’s not how technology works.
Lower costs and higher leverage enable more experimentation, more products, and more startups. The barrier to entry drops. The scope of what’s possible expands.
The result isn’t simply fewer jobs—it’s different jobs.
Those who reskill and adapt will find more opportunity, not less.
The Hardest Group to Transition
Ironically, the most difficult transition isn’t for new graduates.
Early-career engineers are learning AI-assisted development from day one. They don’t have to unlearn old habits.
The harder challenge belongs to mid-career professionals—those with decades of experience built on older workflows. Success for this group depends on willingness to relearn, experiment, and let go of familiar patterns.
The good news? Experience combined with adaptability is incredibly powerful.
The Real Takeaway
Lifelong learning is no longer a personal growth slogan—it’s a survival skill.
Whether through formal education, self-directed study, or hands-on experimentation, the professionals who thrive will be those who:
Stay curious
Embrace change
Invest in depth, not just speed
View AI as a collaborator, not a competitor
The future doesn’t belong to the youngest or the most credentialed. It belongs to those who never stop learning.
