Think. Automate. Transform.

In the evolving landscape of artificial intelligence, the idea of a “brain switch” - a mechanism to activate or deactivate cognitive power on demand -   is no longer just a metaphor. It’s becoming a design principle. Just as the human brain toggles between rest and focus, AI systems are increasingly being built with the ability to regulate their cognitive load, switching between passive observation, active reasoning, and strategic inaction based on context. This concept is already visible in everyday AI applications. Take virtual assistants like Siri , Alexa , or Google Assistant . These systems remain in a low-power “listening” state until activated by a wake word. Once triggered, they switch into a high-cognition mode - parsing language, retrieving data, and executing tasks. This is a literal implementation of a brain switch: conserving energy and attention until a stimulus demands engagement. Autonomous vehicles offer another compelling example. Self-driving cars continuou...

In today’s tech-powered world—where AI transforms healthcare and blockchain reshapes finance—students aspiring to build careers in technology often face a pivotal decision: which academic path best aligns with their goals? Two popular options in India’s digital economy are the BTech in Computer Science and the Bachelor of Computer Applications (BCA), each offering distinct advantages. While both degrees open doors to thriving careers, they differ in scope and depth. BTech in Computer Science offers a multidisciplinary engineering approach, integrating computing with electronics, mathematics, and systems design. In contrast, BCA focuses exclusively on the computer science domain, delivering specialized knowledge in programming, software development, and application design. As emerging technologies like AI, IoT, Blockchain, and Cloud Computing reshape industries, choosing the right programme becomes more than an academic decision—it’s a strategic career move. Understanding the nuance...

In today’s technology-driven world, choosing the right engineering discipline can shape not only your career trajectory but also your intellectual identity. It’s not merely a decision about what you’ll do - it’s about how you’ll think, what you’ll value, and the kind of problems you’ll be drawn to solve. Among the most sought-after degrees are BTech in Software Engineering and BTech in Data Science - each offering distinct opportunities, skill sets, and philosophical orientations toward problem-solving. While both are rooted in computing, they diverge in purpose: one builds systems, the other deciphers them. Software Engineering is the backbone of digital infrastructure. It focuses on designing, developing, testing, and maintaining software applications and platforms that power our everyday lives. Students delve into programming languages like Java, C++, and Python; master system architecture and cloud platforms like AWS and Azure; and adopt agile methodologies to deliver scalable so...

In today’s hyper-connected economy, the fusion of technology and business strategy is no longer optional - it’s foundational. MBA Tech programs, which combine engineering rigor with managerial insight, are equipping a new generation of professionals to lead in complex, tech-centric environments. Whether you're graduating from NMIMS in Mumbai, IE Business School in Madrid, or NYU Tandon in New York, this dual-degree unlocks career paths that demand both technical fluency and strategic foresight. From product innovation to digital transformation, MBA Tech graduates are increasingly seen as the connective tissue between CTOs and CEOs. The career landscape is expansive and evolving. In India, graduates often land roles like Product Manager at Flipkart , Business Analyst at ZS Associates , or Digital Strategy Consultant at Infosys . Globally, similar profiles emerge at companies like SAP in Germany , Salesforce in the U.S. , or Tencent in China , where hybrid thinkers are driving plat...

This month’s my tech series blogs dives into AI’s emotional ripple effects - how it shapes mental health, fuels loneliness, and deepens social isolation. As these tools weave into our daily routines, their psychological impact deserves just as much attention as their technical brilliance. We kicked off with AI Hallucination; today, I’m excited to share the second chapter: AI Psychosis . Let’s keep peeling back the layers. AI hallucination and AI psychosis are metaphorical terms used to describe errant behavior in artificial intelligence, but they differ in scope and implication. AI hallucination refers to an AI generating false or fabricated information that appears plausible - like inventing citations or misquoting facts - often due to gaps in training data or misaligned reasoning. AI psychosis, while not a formal technical term, is sometimes used provocatively to describe more severe, systemic breakdowns in AI behavior, such as persistent delusions, incoherence, or erratic out...

Artificial Intelligence is reshaping the core identity of engineering. What was once a discipline focused on writing code and solving isolated technical problems is now evolving into a strategic, ethical, and design-driven practice. Engineers are no longer just builders - they are becoming architects of intelligent systems and stewards of societal impact. This transformation is fueled by AI tools that automate routine tasks, freeing engineers to engage in higher-order thinking. The age of AI demands a new kind of engineer: one who can think critically, design responsibly, and lead across disciplines. As AI systems take over repetitive coding, engineers are shifting toward systems thinking. They must now design architectures that are scalable, resilient, and capable of integrating machine learning models and real-time data flows. At Shopify, developers use GitHub Copilot to generate boilerplate code, allowing them to focus on refining user experiences and embedding intelligent recomme...

The rise of generative AI has triggered a seismic shift in the software engineering landscape. Tools like ChatGPT, Gemini, Amazon CodeWhisperer, and Cursor can now generate functional code in seconds, automating tasks that once defined entry-level roles. According to a Stanford study, employment for early-career engineers in AI-exposed roles has declined by 13% since late 2022. This isn’t just a productivity boost—it’s a structural rebalancing. Companies no longer hire junior developers to write boilerplate code; instead, they seek engineers who can design systems, debug complex workflows, and orchestrate AI tools strategically. This shift tempts students to skip foundational learning and chase trendy skills like prompt engineering or LLM fine-tuning. But that’s a mistake. Consider the case of a startup that used AI to build its MVP (minimum viable product). While the code worked initially, it quickly became unmanageable—functions were tangled, interfaces inconsistent, and the architec...