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Soundarya Balasubramani
Curious Maverick โข 130K followers
We're leaving India to work at FAANG, while FAANGs are investing tens of billions into infrastructure in India. Context: Google just invested $10B in India to build its AI infrastructure, and they are not alone. Microsoft added $17.5B and Amazon threw in $35B for the same reason. ๐ Some key highlights: โ Building a data center in India costs $7 per watt. โ A mid-level AI engineer in India makes $30K-$55K. โ The same person in the U.S. makes around $180K-$250K. โ That's 70% cheaper for the same work. ๐ Hereโs more highlights: โ India graduates 850,000+ engineers every year. โ The U.S. graduates ~140,000. โ Even Satya Nadella thinks India will have more AI talent than the US by 2027. โ And AI models don't care where they're trained. Google, Microsoft and Amazon aren't just building their satellite offices here. They're also investing in building AI infrastructure in India because it makes better business sense. Between 2021-2023, nearly 1,000 international companies set up in India. All of this could mean: The career gap that used to justify any cost to reach the U.S. is narrowing fast. โ ๐ฟ Re-post if this resonated. ๐ I share weekly stories of impactful people, companies, and art. Join 34,000+ at Curious Maverick
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Arvind Kumar
Axis Bank โข 4K followers
๐ฃ GitHub CEO: AI Will Write 90% of Code by 2030 Max โ Adapt Now or Be Left Behind. For developers across India and the world, the message is loud and clear: Reinvent or risk irrelevance. AI is no longer โthe futureโ. Itโs the present & we are entering hard tech era . From Bengaluru to San Francisco, developers are already witnessing the shift: AI tools are writing code, debugging, generating test cases, and even suggesting architectural decisions. But instead of job losses, something unexpected is happening โ ๐ก Developers are becoming more ambitious, creative, and strategic. A recent field study by GitHub CEO Thomas Dohmke reveals a fascinating transformation underway: ๐ 4 Stages of AI Evolution for Developers: ๐นSkeptic: Tries AI tools once or twice, calls them โcool but buggy.โ ๐นExplorer: Starts using AI for small tasks โ snippets, debugging. ๐นCollaborator: Begins co-creating with AI, working across files, tools, and models. ๐นStrategist: Designs systems, orchestrates agents, reviews AI output โ and focuses on outcomes, not just code. One participant said it best: ๐ง โWeโre becoming less code producers, more code enablers. My next title might be Creative Director of Code.โ ๐จ Key Insights From the Study: ๐น AI is on track to write 90% of code in the next 2โ5 years. ๐น Developers who embrace AI feel more optimistic and confident about their careers. ๐น The focus is shifting from writing code to designing systems, prompting AI agents, and validating results. ๐น AI fluency is the new must-have skill โ not just how to code, but how to collaborate with AI. ๐ Implications for Education (India, take note ๐): Teaching just syntax or theory wonโt cut it anymore. CS students and aspiring engineers must now learn to: โ Prompt and guide AI effectively โ Debug and critique AI-generated code โ Think in systems, not just scripts โ Collaborate with AI tools, not avoid them Assignments must shift from: โWrite a loopโ to โHereโs what AI wrote โ whatโs wrong with it?โ or โImprove this spec so AI can build what you intended.โ ๐ Job Outlook? Surprisingly Strong. While traditional roles may evolve, the U.S. Bureau of Labor Statistics still projects an 18% growth in software developer jobs โ nearly 5x faster than the average occupation. The same trend echoes globally, including in Indiaโs booming tech sector. But the definition of a โdeveloperโ is changing. The most successful ones are: ๐ง Orchestrating AI agents ๐ง Applying deeper product thinking ๐ฆ Building systems, not just code files โ So whether you're in Bengaluru, San Francisco, or Singapore โ here's the reality: AI isnโt here to replace you. Itโs here to work with you โ if you know how to use it. #AI #SoftwareEngineering #GitHub #IndiaTech #FutureOfWork #DeveloperLife #AIRevolution #PromptEngineering #TechCareers #EdTech #CreativeDirectorOfCode #India #IT #hardtech
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Nitin Sharma
Self-employed โข 990 followers
Oracle is going to lay off employees in the US and India. Not because the company is failing but because itโs betting everything on AI. Theyโre cutting roles in their cloud division to free up billions for Project Stargate โ a $500 billion AI infrastructure partnership with OpenAI and SoftBank. In plain English: Oracle is building the โfactoriesโ where the next generation of AI will be trained and run. Well, this is becoming the playbook for big tech: โ Cut human costs in areas where AI will soon outperform. โ Reallocate capital into AI infrastructure, models, and platforms. โ Capture the future market before competitors do. The reality? โข AI is moving faster than any past tech revolution. โข It doesnโt just replace jobs, it eliminates entire categories of work. โข As AI capabilities explode, companies will grow bigger, more efficientโฆ with far fewer people. You see, weโre heading into a future where: โ A handful of companies control the AI infrastructure. โ Most routine jobs vanish. โ The value of your skills depends entirely on how โAI-proofโ they are. The harsh truth? Weโre heading into a future where growth and profits will come from machines, not manpower. The winners will be companies that adapt fastest, and individuals who learn to work with AI, not compete against it. ๐ช๐ฎ๐ป๐ ๐๐ผ ๐น๐ฒ๐ฎ๐ฟ๐ป ๐บ๐ผ๐ฟ๐ฒ ๐ฎ๐ฏ๐ผ๐๐ ๐๐๐ถ๐ป๐ด ๐๐ ๐ฝ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฎ๐น๐น๐? ๐ฆ๐๐ฏ๐๐ฐ๐ฟ๐ถ๐ฏ๐ฒ ๐๐ผ ๐บ๐ ๐ป๐ฒ๐๐๐น๐ฒ๐๐๐ฒ๐ฟ: https://lnkd.in/dcx8YBYP #AI #artificialintelligence #Startups #DeepMind #OpenAI #jobs #technology #productivity #oracle
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Simardeep S.
Department of Nationalโฆ โข 4K followers
Pinterest ๐๐๐๐ ๐ฆ๐ต๐ผ๐๐ฒ๐ฑ ๐๐ผ๐ ๐๐ผ ๐๐ฐ๐๐๐ฎ๐น๐น๐ ๐ ๐ถ๐ด๐ฟ๐ฎ๐๐ฒ ๐ณ๐ฟ๐ผ๐บ ๐๐ฎ๐ฑ๐ผ๐ผ๐ฝ ๐๐ผ ๐๐๐ฏ๐ฒ๐ฟ๐ป๐ฒ๐๐ฒ๐ (๐๐ป๐ฑ ๐ก๐ผ๐ ๐ ๐ฒ๐๐ ๐๐ ๐จ๐ฝ) Pinterest dropped a detailed write-up on how they moved their entire data processing platform from Hadoop to Kubernetes. 70% migrated already, rest coming by year-end. Instead of just moving Spark jobs to K8s and hoping for the best, they built "Moka" - a proper platform with all the pieces in place: job submission, resource management, monitoring, the works. They're running ๐๐ฅ๐ ๐ฎ๐ป๐ฑ ๐ 86 ๐๐ถ๐ฑ๐ฒ ๐ฏ๐ ๐๐ถ๐ฑ๐ฒ. Not everything works great on ARM, but when it does, the cost savings are real. They're picking the right instance type per workload instead of going all-in on one architecture. ๐๐ฝ๐ฎ๐ฐ๐ต๐ฒ ๐๐ฒ๐น๐ฒ๐ฏ๐ผ๐ฟ๐ป for shuffle replaced Hadoop's external shuffle service and delivered 5% better performance across the board. ๐ฌ๐๐ป๐ถ๐๐ผ๐ฟ๐ป ๐๐ฐ๐ต๐ฒ๐ฑ๐๐น๐ฒ๐ฟ brought all the queue management and resource controls from YARN over to Kubernetes - critical for keeping SLAs during the migration. They built a "dry run" system that automatically runs every production job on both the old Hadoop cluster AND the new K8s platform, then compares the results. Data sizes, checksums, everything. Only migrate jobs that pass validation. This took years and involved teams across the entire engineering org. Moving from Java 8 to 11, recompiling libraries for ARM, making sure containers work exactly like the old Hadoop setup - there's no shortcuts at this scale. But they pulled it off without breaking production, which honestly is the real achievement here. Worth reading the article by Soam Acharya, Rainie Li: https://lnkd.in/eYN_gmNX #BigData #Kubernetes #Spark #DataEngineering