Computer Science: The Future of Leverage and Freedom

Is the Computer Science degree changing? With AI handling more of the day-to-day coding, many of us are asking what the role of a CS professional will look like in the next few years. I believe we are seeing a shift in the field. We are moving from being writers of code to becoming Intelligence Architects: the people who design how AI logic connects to the physical world. In my latest article, I explore why the CS degree is entering a vital new phase. The focus is shifting toward Cyber-Physical Systems (CPS), where software meets physical reality. Some of the points I’ve highlighted for discussion: → The logic behind the machine: Why fundamental concepts like Turing’s laws mean human oversight remains a mathematical necessity. → Expanding our toolkit: The value of adding physics and control systems to the CS curriculum to handle real-world machines. → The collaborative "brain": How CS professionals can lead multidisciplinary teams by integrating the work of mechanical and electrical engineers. → Digital Twins: Moving from building standalone apps to creating digital models that manage complex physical systems. This article is a look at how CS graduate might update the skills and the CS classrooms to stay ahead of these changes. I also include my case study from my experience, such as how a CS graduate plays a role in a complex project involving Cyber-Physical Systems and multidisciplinary teams (from Electrical to Mechanical Engineering), with a case study on Airline Maintenance systems. Read the full article here: https://lnkd.in/guSe_gGp #ComputerScience #AI #CyberPhysicalSystems #STEM #Engineering #TechTalk #FutureOfWork

We’ve spent years building some of the powerful supercomputers. Petaflop-scale. Real infrastructure. And through all of it one problem kept showing up: The engineers who need these skills have nowhere to learn them properly. Not HPC. Not GPU infra. Not the compute layer that actually powers modern AI. Theory heavy courses. Outdated content. No one teaching what production actually looks like. So we built something different. Introducing STUDIO COMPUTE . A training studio for engineers, freshers, researchers, and IT professionals who want to build real skills in: 🖥️ HPC & Supercomputing 🤖 AI / ML 📊 Data Science ⚡ GPU Infra & Data Centers Not a coaching institute. Not a recorded course dumped on a portal. A studio. Small cohorts. Practical curriculum. Built by people who’ve done this at national scale. Follow STUDIO COMPUTE on LinkedIn to be the first to know when we launch. If you know someone who belongs here — tag them below. 👇

🚀 From EE to CS: My Playbook for Transitioning into AI & Tech : We recently explored a powerful resource — “EE to CS Playbook” by Dheeraj Mishra, IIT Bombay — and it perfectly captures what many Electrical Engineering students are trying to do today: transition into Computer Science, AI, and cutting-edge tech roles. Here’s the reality 👇 The world is moving fast toward AI, systems, and software-driven innovation. And as EE students, we already have a hidden advantage — strong math, problem-solving, and systems thinking. 🔹 Leverage your EE foundation Linear Algebra, Multivariable Calculus ,Probability & Statistics— these are GOLD for ML, AI, and data science. 🔹 Master Core CS Fundamentals Data Structures & Algorithms, Operating Systems, and Computer Networks are non-negotiable. 🔹 Build Real Projects (Not Just Theory) Projects > Certificates Think: ML systems, embedded AI, large-scale data pipelines. 🔹 Transition Smartly, Not Randomly Pick a direction: AI/ML Research Software Engineering Systems / Embedded AI 🔹 Consistency beats intensity Daily focused effort > random bursts of motivation. ⚡ My Personal Insight: The EE → CS transition is NOT about starting from zero. It’s about connecting what you already know to what the industry needs. If done right, this path can lead to: Top research roles High-impact AI startups Elite tech companies Don’t just “learn coding” — build something that very few people in the world can build. If you're from EE and thinking about moving into AI/CS, this playbook is worth your time. Copy this Link & paste on Google to see big picture : file:///C:/Users/dheer/Downloads/ee_to_cs_playbook.html by Dheeraj Mishra Sir. Let’s build the future. 💻⚡ #AI #MachineLearning #ElectricalEngineering #ComputerScience #GATE2027 #DeepLearning #TechCareers #LearningJourney

Do we still need a CS degree? I keep seeing the same tired debate: "AI killed computer science" versus "You still need the degree." Both sides miss what's actually happening. Three numbers stopped me cold: 43% of working developers are self-taught But only 1 in 700 hires at "skills-first" companies happen without degrees 53% of employers dropped degree requirements on paper The policy changed faster than the practice. The vibe coding reality: - Andrej Karpathy's right—programming is shifting from "how" to "what." - Andrew Ng just launched "Vibe Coding 101," taking absolute beginners to a deployed web app in under 94 minutes. But Satya Nadella got it exactly right: AI "lowers the floor" for entry, but "raises the ceiling" on what sophisticated engineers need to know. If you can't audit what the AI generates, you're not building—you're hoping. The Jensen Huang pivot surprised me. Nvidia's CEO didn't say "skip college." He said he'd study physics instead of CS today. Not because coding's dead, but because the next wave of AI requires understanding physical systems, causality, and first-principles reasoning—the kind of rigorous thinking a technical degree builds, even if the specific major changes. Let's talk numbers. A CS degree from a top US program runs $40,000 to $130,000 in tuition alone. Add four years of foregone income and you're looking at $300,000+ in real cost. The degree still costs $300,000. It just matters less than it used to. Here's my take: → Building AI infrastructure, ML research, or distributed systems at scale? Get the degree. Seriously, consider grad school. You can't fake linear algebra or adversarial security thinking. → Building products, starting companies, or switching careers fast? Ship things. Your GitHub matters more than your alma mater. → Not sure yet? Start free. Add structure once you're committed. Fill theoretical gaps later with targeted programs. Don't spend six figures to find out if you like coding. The gatekeepers are losing their keys. What you build with the open door—that's what counts. Link in the first comment 👇 #SoftwareEngineering #CareerAdvice #ArtificialIntelligence #TechHiring #ContinuousLearning #CSEducation #VibeCoding #TechCareers

𝗖𝗦 𝗠𝗮𝗷𝗼𝗿 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜: 𝗪𝗵𝘆 𝘁𝗵𝗲 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗠𝗼𝘃𝗲𝗱 𝗪𝗮𝘆 𝗕𝗲𝘆𝗼𝗻𝗱 𝗦𝗪𝗘 Namrata & I are super proud that our daughter Nirvika will start at Georgia Institute of Technology as a CS Major this fall. But is CS still the right bet in a world where the common wisdom is that AI has eaten software engineering (SWE)? My answer is YES! Key to note that AI has solved the coding problem but not yet the broader SWE problem. And CS is so much more than either. 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗖𝗦 𝗠𝗮𝗷𝗼𝗿 𝟯𝟬 𝗬𝗲𝗮𝗿𝘀 𝗔𝗴𝗼 I started as a CS Major without ever having used a computer. Small town India had no such luxury. By senior yr, I realized that coding was the most practical way to express ideas I was learning in Algorithms, Operating Systems, Computer Architecture, Compilers, Networking and Parallel Computing. I never fell in love with coding itself. I fell in love with the powerful machines it let me control. Same story played out in grad school as I went deeper into Distributed Systems & Web OS. 𝗙𝗶𝗻𝗱𝗶𝗻𝗴 𝗠𝘆 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝗦𝗪𝗘 Campus recruiting is where I learned that most CS grads become SWEs. I took the job because I needed to start earning. My worst fears came true when I realized I hate writing EJB code. I transitioned into Product Management. My CS education, my stint as a SWE and my drive to learn what makes products successful all compounded into something I continue to remain excited about till date. 𝗖𝗦 𝗠𝗮𝗷𝗼𝗿 𝗪𝗮𝘀 𝗡𝗲𝘃𝗲𝗿 𝗮 𝗦𝗪𝗘 𝗗𝗲𝗴𝗿𝗲𝗲 SWE was one use case of CS that happened to be the most financially obvious, which is why so many of us ended up there by default rather than by design. Since AI now handles coding, the CS Major is finally unburdened to aim at the aspirational things it was always about. Best CS programs are doubling down. Georgia Tech is a good example with its Threads curriculum that lets students specialize across 9 areas of computing. Its AI Makerspace with NVIDIA gives undergraduates access to supercomputing resources once reserved for graduate researchers. 𝗠𝗼𝗱𝗲𝗹 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 𝗮𝗻𝗱 𝗔𝗴𝗲𝗻𝘁 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿 The roles I would point any driven CS grad toward today are model builder and agent builder, because both reward what strong CS education has always taught. Model builders need linear algebra, probability & stats, optimization, parallel computing, and GPU architecture. Agent builders need distributed data, memory & state management, concurrency, and careful reasoning about failure modes. Both need evaluation & feedback loops. None of these are coding problems. Treat CS as a systems discipline so that you are able to build the next decade of computing infra that the world runs on. To Nirvika, and to every student entering a CS program this fall, the frontier has moved way beyond SWE. You get there by embracing AI to learn CS 10x faster and deeper than your parents' generation did!

In the age of AI, software engineering is evolving in a way that raises the bar for entry. Even with strong fundamentals, it is becoming more demanding to stand out. Not long ago, completing a 6–12 month bootcamp or short course was often enough to secure an entry-level role. That pathway is quickly fading. As AI continues to introduce deeper layers of abstraction, the expectations placed on engineers are shifting. It is no longer sufficient to understand syntax or work within a single framework. Engineers are now expected to grasp systems holistically—how components interact, scale, and fail in real-world environments. This shift makes it increasingly difficult for beginners with only surface-level knowledge to compete. Success now depends on depth of understanding, adaptability, and the ability to think beyond tools toward underlying principles.

𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 ≠ 𝐜𝐨𝐝𝐢𝐧𝐠. Coding is a tool, used across multiple disciplines such as engineering, mathematics, computer science, and physics. Computer science is a 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘧𝘪𝘤 𝘥𝘪𝘴𝘤𝘪𝘱𝘭𝘪𝘯𝘦 about information processes and computation itself. 𝘞𝘩𝘢𝘵 𝘤𝘢𝘯 𝘣𝘦 𝘤𝘰𝘮𝘱𝘶𝘵𝘦𝘥, 𝘩𝘰𝘸 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵𝘭𝘺, 𝘢𝘯𝘥 𝘸𝘩𝘺? 𝐂𝐒 𝐢𝐬 𝐚 𝐟𝐢𝐞𝐥𝐝 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐚𝐫𝐞 𝐬𝐭𝐮𝐝𝐢𝐞𝐝 (𝐚𝐧𝐝 𝐦𝐨𝐫𝐞): 🏛️ Abstract models of machines and processes are rigorously defined, and used to represent and reason about computational problems. 🧪 The scientific method is applied to evaluate the performance of systems, algorithms, and designs. 📐 Algorithms are mathematical descriptions with provable properties — not just Java or Python code. 📊 Problems are classified by computability and complexity — what can be solved, and how efficiently. 📜 Programming languages are developed and analysed to understand their semantics, correctness, expressiveness, type systems, usability, and other properties. 🗂️ Information representation and data structures are studied to enable efficient processing and analysis. 🔁 Concurrency and parallelism are studied to understand how multiple processes interact and execute simultaneously. 🏗️ The principles behind designing and engineering complex, scalable software systems are studied, including modularity, abstraction, reliability, and resilience. ⎯⎯⎯⎯⎯ ℹ️ 𝘗𝘦𝘳𝘩𝘢𝘱𝘴 𝘳𝘦𝘴𝘪𝘭𝘪𝘦𝘯𝘤𝘦 𝘯𝘦𝘦𝘥𝘴 𝘢 𝘴𝘵𝘳𝘰𝘯𝘨𝘦𝘳 𝘧𝘰𝘤𝘶𝘴 𝘨𝘰𝘪𝘯𝘨 𝘧𝘰𝘳𝘸𝘢𝘳𝘥: https://lnkd.in/eghbwsvy ℹ️ A𝘯 𝘦𝘹𝘢𝘮𝘱𝘭𝘦 𝘭𝘪𝘴𝘵 𝘰𝘧 𝘊𝘚 𝘴𝘶𝘣𝘫𝘦𝘤𝘵𝘴 𝘢𝘵 𝘊𝘢𝘮𝘣𝘳𝘪𝘥𝘨𝘦: https://lnkd.in/ejDTrdjP ℹ️ 𝘛𝘩𝘦 𝘣𝘳𝘰𝘢𝘥𝘦𝘳 𝘪𝘮𝘱𝘢𝘤𝘵 𝘰𝘧 𝘤𝘰𝘮𝘱𝘶𝘵𝘢𝘵𝘪𝘰𝘯 𝘰𝘯 𝘴𝘰𝘤𝘪𝘦𝘵𝘺 𝘢𝘭𝘴𝘰 𝘯𝘦𝘦𝘥𝘴 𝘵𝘰 𝘣𝘦 𝘢 𝘤𝘰𝘳𝘦 𝘱𝘢𝘳𝘵 𝘰𝘧 𝘵𝘩𝘦 𝘴𝘺𝘭𝘭𝘢𝘣𝘶𝘴 (𝘴𝘦𝘦 𝘣𝘦𝘭𝘰𝘸). ⎯⎯⎯⎯⎯ In a world of AI-assisted coding, this distinction deserves greater emphasis, and university courses should better reflect computer science as a #scientific #discipline.

I think Lavindra's point is well made, and would add that you can also see computer science as an engineering discipline. Coding is the tool; many computer science graduates are really software engineers who use coding as one of their many tools to solve problems. They have a deep understanding of coding, and can write code if need be, but it's their problem solving, project management and other higher level skills that really add value.

What things a CS student has to do to be successful in 2026 ? CS fundamentals(CN, OS, COA....) DSA Fullstack development AI/ML System design Build projects Do open source Participate in Hackathons Good GPA in semester exams ?? Meanwhile someone says that's not enough you also need to keep promoting urself on LinkedIn, X .so that you don't miss out in this competitive market !! Most of the B. tech CS students including me have no idea what and how they should me doing things and lots of misinformation spread everywhere makes things even worse. So, just to bring some clarity for all the CS students, if would like to ask all the big tech companies, senior tech professionals and all who those in place of answering this question, What skills exactly a CS students must build in this AI era to stay relevant and be able to build a good future ahead? please let us all know. Google Microsoft NVIDIA Accenture Apple Meta OpenAI Apna College takeUforward

“Coding is not the job of an engineer.” 🤯 Wait… what? This completely flipped my thinking. As a BTech student, I always believed: 👉 Learn coding = become a great engineer But this made me realize something deeper 💭 Coding is just a **tool**. The real job is **thinking, solving, building ideas**. 💡 Anyone can write code today (thanks to AI 🤖) But not everyone can: * Understand the problem deeply * Think of the right solution * Build something that actually works in real life 🔥 That’s the difference. We’re focusing so much on: “Which language should I learn?” Instead of asking: “Can I solve this problem efficiently?” 📌 My takeaway: Don’t just learn to code… Learn to **think like an engineer** 🧠⚡ Because in the end, Code is written by machines… But solutions are created by humans. This really changed my perspective 🚀 What do you think — is coding enough anymore? 👇 #Engineering #AI #Learning #BTech #ProblemSolving