Tejas Chopra on Building for Impact
Tejas Chopra builds for scale—and teaches others how. He designed metadata at Datrium, re-architected storage at Box, and now leads ML and storage platforms at Netflix, delivering reliability under pressure. He extends that mission beyond systems, co-founding EnsolAI and GoEB1 to help people put AI to work for growth.
As one of our newest TechArena voices of innovation, Tejas cuts through the hype cycle with a builder’s lens: agent reliability, how to separate research from product, and why quiet, iterative work actually moves the needle. Expect lessons that translate—from billion-dollar infra teams to two-person startups, grounded in real problems, not buzzwords.
Q1: Can you tell us a bit about your journey in tech?
A1: I’ve always been drawn to systems that scale. From building metadata storage engines at Datrium to re-architecting storage infrastructure at Box, and now leading machine learning and storage platforms at Netflix, my focus has been on creating reliability at scale. Over time, that curiosity extended beyond large-scale systems into how technology can drive opportunity—which led me to found EnsolAI and GoEB1, both built to help people leverage AI for meaningful professional growth.
Q2: Looking back at your career path, what’s been the most unexpected turn that shaped who you are today?
A2: Moving from deep systems engineering into entrepreneurship. Building startups taught me a new kind of scalability—not of data or compute, but of people, purpose, and conviction. It forced me to think like both an engineer and a customer—and that ultimately made me a better technologist.
Q3: How do you define “innovation” in today’s rapidly evolving tech landscape? Has your definition changed over the years?
A3: Innovation, for me, used to mean cutting-edge algorithms or new architectures. Today it means solving a real problem in a way that’s repeatable, cost-aware, and human-centric. The best innovations don’t always come from new technology—they come from seeing a familiar problem differently.
Q4: What’s one emerging technology or trend that’s flying under the radar but will have significant impact in the next 2–3 years?
A4: We’re overlooking agent reliability—ensuring AI agents act safely, predictably, and accountably. As multi-agent systems become mainstream, frameworks for trust, observability, and control will determine which companies sustain long-term adoption and which don’t.
Q5: When you’re evaluating new ideas or technologies, what’s your framework for separating genuine innovation from hype?
A5: I ask three questions: Does it remove a pain point or just sound exciting? Can it scale sustainably? And would someone pay for it today? If all three are yes, build it. Otherwise, it’s research—not a product.
Q6: What’s the biggest misconception you encounter about innovation in the tech industry?
A6: That innovation has to look glamorous. In reality, it’s often quiet, iterative, and unglamorous. The real breakthroughs come from people fixing what everyone else tolerates.
Q7: How do you see the relationship between AI advancement and human creativity evolving—competitors or collaborators?
A7: Collaborators. AI amplifies creative range but can’t replace intent or taste; the human role shifts from generating to guiding—shaping AI outputs with context, nuance, and moral clarity.
Q8: If you could solve one major challenge facing the tech industry today, what would it be and why?
A8: Bridging the gap between technical capability and access. So much potential is locked behind systems, jargon, and privilege. If we can make advanced tools—like AI—accessible and affordable, we democratize innovation itself. That belief drives both EnsolAI and GoEB1.
Q9: What’s a book, podcast, or idea that fundamentally changed how you think about technology or business?
A9: Business Sutra by Devdutt Pattanaik. It reframed how I think about leadership and innovation—not as rigid hierarchies of control, but as dynamic relationships among purpose, people, and context. It taught me that how we think determines what we build. That lens helps me design systems and teams that are adaptable, not brittle.
Q10: When you’re facing a particularly complex problem, what’s your go-to method for finding clarity?
A10: I break it down to constraints and first principles. What’s unchangeable? What’s optional? Once you know that, complexity usually reduces to a few core decisions. I write, diagram, and simulate trade-offs until the signal emerges.
Q11: Outside of technology, what hobby or interest gives you the most inspiration for your professional work?
A11: Travel. Seeing how people solve everyday problems with limited resources constantly resets my design lens. It’s humbling and practical—both qualities tech needs more of.
Q12: What excites you most about joining the TechArena community, and what do you hope our audience will take away from your insights?
A12: TechArena brings together people who care about depth over noise. I’m excited to share learnings from building at Netflix scale and from starting lean, self-funded ventures. I hope readers take away that innovation can happen anywhere—from billion-dollar infra teams to two-person startups—if you focus on solving real, painful problems well.
Q13: If you could have dinner with any innovator from history, who would it be and what would you ask them?
A13: Pãnini or Ãryabhata. Pãnini’s precision in defining Sanskrit grammar mirrors the elegance we seek in programming languages today, while Ãryabhata’s mathematical imagination still informs how we model the world. I’d ask how they balanced logic and intuition—how they derived universal truths from patterns in language and nature. That balance is what all great technology ultimately strives for.
Tejas Chopra builds for scale—and teaches others how. He designed metadata at Datrium, re-architected storage at Box, and now leads ML and storage platforms at Netflix, delivering reliability under pressure. He extends that mission beyond systems, co-founding EnsolAI and GoEB1 to help people put AI to work for growth.
As one of our newest TechArena voices of innovation, Tejas cuts through the hype cycle with a builder’s lens: agent reliability, how to separate research from product, and why quiet, iterative work actually moves the needle. Expect lessons that translate—from billion-dollar infra teams to two-person startups, grounded in real problems, not buzzwords.
Q1: Can you tell us a bit about your journey in tech?
A1: I’ve always been drawn to systems that scale. From building metadata storage engines at Datrium to re-architecting storage infrastructure at Box, and now leading machine learning and storage platforms at Netflix, my focus has been on creating reliability at scale. Over time, that curiosity extended beyond large-scale systems into how technology can drive opportunity—which led me to found EnsolAI and GoEB1, both built to help people leverage AI for meaningful professional growth.
Q2: Looking back at your career path, what’s been the most unexpected turn that shaped who you are today?
A2: Moving from deep systems engineering into entrepreneurship. Building startups taught me a new kind of scalability—not of data or compute, but of people, purpose, and conviction. It forced me to think like both an engineer and a customer—and that ultimately made me a better technologist.
Q3: How do you define “innovation” in today’s rapidly evolving tech landscape? Has your definition changed over the years?
A3: Innovation, for me, used to mean cutting-edge algorithms or new architectures. Today it means solving a real problem in a way that’s repeatable, cost-aware, and human-centric. The best innovations don’t always come from new technology—they come from seeing a familiar problem differently.
Q4: What’s one emerging technology or trend that’s flying under the radar but will have significant impact in the next 2–3 years?
A4: We’re overlooking agent reliability—ensuring AI agents act safely, predictably, and accountably. As multi-agent systems become mainstream, frameworks for trust, observability, and control will determine which companies sustain long-term adoption and which don’t.
Q5: When you’re evaluating new ideas or technologies, what’s your framework for separating genuine innovation from hype?
A5: I ask three questions: Does it remove a pain point or just sound exciting? Can it scale sustainably? And would someone pay for it today? If all three are yes, build it. Otherwise, it’s research—not a product.
Q6: What’s the biggest misconception you encounter about innovation in the tech industry?
A6: That innovation has to look glamorous. In reality, it’s often quiet, iterative, and unglamorous. The real breakthroughs come from people fixing what everyone else tolerates.
Q7: How do you see the relationship between AI advancement and human creativity evolving—competitors or collaborators?
A7: Collaborators. AI amplifies creative range but can’t replace intent or taste; the human role shifts from generating to guiding—shaping AI outputs with context, nuance, and moral clarity.
Q8: If you could solve one major challenge facing the tech industry today, what would it be and why?
A8: Bridging the gap between technical capability and access. So much potential is locked behind systems, jargon, and privilege. If we can make advanced tools—like AI—accessible and affordable, we democratize innovation itself. That belief drives both EnsolAI and GoEB1.
Q9: What’s a book, podcast, or idea that fundamentally changed how you think about technology or business?
A9: Business Sutra by Devdutt Pattanaik. It reframed how I think about leadership and innovation—not as rigid hierarchies of control, but as dynamic relationships among purpose, people, and context. It taught me that how we think determines what we build. That lens helps me design systems and teams that are adaptable, not brittle.
Q10: When you’re facing a particularly complex problem, what’s your go-to method for finding clarity?
A10: I break it down to constraints and first principles. What’s unchangeable? What’s optional? Once you know that, complexity usually reduces to a few core decisions. I write, diagram, and simulate trade-offs until the signal emerges.
Q11: Outside of technology, what hobby or interest gives you the most inspiration for your professional work?
A11: Travel. Seeing how people solve everyday problems with limited resources constantly resets my design lens. It’s humbling and practical—both qualities tech needs more of.
Q12: What excites you most about joining the TechArena community, and what do you hope our audience will take away from your insights?
A12: TechArena brings together people who care about depth over noise. I’m excited to share learnings from building at Netflix scale and from starting lean, self-funded ventures. I hope readers take away that innovation can happen anywhere—from billion-dollar infra teams to two-person startups—if you focus on solving real, painful problems well.
Q13: If you could have dinner with any innovator from history, who would it be and what would you ask them?
A13: Pãnini or Ãryabhata. Pãnini’s precision in defining Sanskrit grammar mirrors the elegance we seek in programming languages today, while Ãryabhata’s mathematical imagination still informs how we model the world. I’d ask how they balanced logic and intuition—how they derived universal truths from patterns in language and nature. That balance is what all great technology ultimately strives for.
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