Optical computing is a completely different paradigm to solve the same problem - swapping electrons for photons, promising blazing speed and profound energy efficiency. As silicon-based computing strains under the demands of AI and data centers, optical computing promises an interesting alternative.

Unlike conventional systems, where electrons trudge through silicon transistors, optical computing uses light - lasers, lenses, and photonic circuits - to encode and process data. Photons move at the speed of light, slashing latency, and can carry multiple signals at once using different wavelengths or polarizations. This makes them perfect for parallel tasks like AI training.

The energy argument is even stronger: photonic systems bypass the resistive heat that throttles electronic chips, potentially slashing power use by orders of magnitude. With data centers now consuming over 1% of global electricity, this efficiency is a potential game changer.

The challenges, though, are steep. With semiconductors, we have decades of innovation, and infrastructure for classical computing. However, melding photonic components with existing silicon infrastructure is like fitting a jet engine into a horse-drawn cart. Crafting reliable, cost-effective optical transistors and memory systems is still a research frontier, far from mass production. Converting data between optical and electronic realms adds latency, dulling the speed advantage. The supply chain for photonic chips is embryonic, and manipulating light at nanoscale demands exacting precision. These aren’t mere tweaks - they call for a fundamental overhaul of how we design and build computers.

If there is sufficient momentum around this technology, optical computing could be a bold leap that could drive our AI future. It demands we reimagine computation from the photon up, balancing audacious potential with hard-nosed practicality.

What do you think? Will optical computing light the way forward?

Image credit: Nanophotonics (2025). DOI: 10.1515/nanoph-2024-0513

I couldn’t help but wonder: does that dog understand the physics of the throw, the arc, the spin, the gravity, or is this all just instinct at work? That moment sparked a deeper reflection on what “understanding” really means, not just for dogs, but for AI and even ourselves.

Let’s dig into this… When the human threw the Frisbee, the dog seemed to know exactly where it would land, darting to the spot with perfect timing. A 2003 Oxford Scholarship study offers some insight: dogs use something called a linear optical trajectory, tracking the Frisbee’s movement against the background at a constant speed to intercept it. It’s a mental shortcut, not a theoretical grasp of physics. That dog isn’t calculating aerodynamics or gravity; it’s just relying on a practical trick that works. So, does that count as understanding?

This question sent me down a rabbit hole, leading to a broader debate about predictive power versus explanatory understanding. In the world of AI, this tension feels especially relevant. I recently came across a perspective from Ilya Sutskever, shared in a 2023 Ted Talk, where he argued that high predictive accuracy equates to understanding. Think about how large language models predict the next word in a sentence; they’re scarily good at it. But is that true understanding, or just pattern recognition? On the other hand, some statistical models can explain phenomena without being predictive at all, challenging the idea that prediction alone tells the whole story. Just like the dog can predict the Frisbee’s path but can’t explain why it falls, an AI might nail a forecast without grasping the underlying “why.”

What really caught my attention was the concept of “world models” in AI, a hot topic in 2025 research. These models aim to go beyond mere prediction, learning through observation and reasoning to mimic human-like thinking. Imagine an AI that doesn’t just guess the next word but understands the context of a conversation the way we do. Yet, even these advanced systems struggle to balance prediction with true explanatory depth. It’s a reminder that understanding might not be a single, perfect ideal; maybe it’s a spectrum, stretching from instinctive prediction to deep, explanatory insight.

As I watched that dog at the park, tail wagging, proudly trotting back with the Frisbee, I couldn’t help but wonder: whether it’s a pup catching a toy or an AI generating text, perhaps the real question isn’t whether they understand physics or language in the way we do. Maybe it’s about how we define understanding itself. Is predictive power enough to call something intelligent, or are we missing a deeper layer?

Known as "The Great Explainer," Richard Feynman, a Nobel Prize-winning physicist, had a rare gift: he could take the most complex ideas in physics and make them feel like a conversation with a curious friend. His approach - rooted deeply in first principles, relentless curiosity, and a refusal to accept anything on faith - transformed how we think about physics and learning.

Feynman’s genius wasn’t confined to equations and theory. It was in his humility, his playful spirit, and his insistence that true understanding comes from building knowledge from the ground up. As Freeman Dyson once noted, Feynman "refused to take anybody’s word for anything," choosing instead to rediscover physics on his own terms. This led to groundbreaking contributions, like his work in quantum mechanics and the development of Feynman diagrams, which revolutionised how we visualise particle interactions.

But what makes Feynman timeless is his ability to inspire. His famous phrase, "What I cannot create, I do not understand," found on his blackboard at the time of his passing in 1988, captures his philosophy: real understanding requires grappling with ideas until you can rebuild them yourself. This mindset resonates deeply with me, both in my work at ASML and in how I approach learning every day.

Before I moved to a new department at ASML, my first team gifted me Feynman’s Lectures on Physics, a cornerstone of scientific education. These volumes, now some of my most prized possessions, feel less like textbooks and more like an invitation to explore the universe with a guide who’s endlessly curious and refreshingly honest.

In this post, I want to dive into some of my favourite Feynman works and stories, each a window into his unique mind. From lectures that sparked my career to videos that capture his awe for the world, these moments show why Feynman remains a hero for scientists and non-scientists alike.

1. An Actor’s Perspective: Alan Alda on Feynman’s Magic

In his 2002 Caltech commencement address, actor Alan Alda shared a vivid portrait of Feynman that captures why he was so beloved. Alda, who portrayed Feynman in the play QED, described him as a man who combined intellectual rigor with a childlike sense of wonder. In the speech (available here), Alda recounts how Feynman’s curiosity was infectious, whether he was cracking safes at Los Alamos or explaining quantum electrodynamics to a room of students.

What struck me most about Alda’s reflection is how he highlighted Feynman’s authenticity. Feynman never pretended to know more than he did. If he didn’t understand something, he’d admit it - whether to a Nobel laureate or a cab driver. This humility, paired with his ability to make science feel like a shared adventure, is what made him such a powerful communicator. Alda’s speech reminds us that Feynman’s legacy isn’t just in his discoveries but in how he invited everyone to join him in asking questions.

2. “Plenty of Room at the Bottom”: The Spark for Nanotechnology

Feynman’s 1959 lecture, “Plenty of Room at the Bottom” (available here), is a visionary essay that arguably laid the foundation for nanotechnology. In it, he imagined a world where we could manipulate matter at the atomic scale, from writing the entire Encyclopaedia Britannica on the head of a pin to building tiny machines that could revolutionise medicine and computing.

Reading this lecture as a young scientist was a turning point for me. Feynman’s ability to see possibilities where others saw limitations was electrifying. His questions beautifully - “Why cannot we write the entire 24 volumes of the Encyclopaedia Britannica on the head of a pin?”. These were not just thought experiments; but a call to action.

This essay is a testament to human ingenuity and the power of asking “what if?” Whether you’re in tech, art, or any field, Feynman’s invitation to explore the “bottom” of things - literally and figuratively - will spark your imagination.

3. The Mystery of Rubber Bands: Feynman’s Playful Clarity

One of my favourite Feynman moments is his explanation of why rubber bands work, captured in this delightful video watch here. With his signature enthusiasm, Feynman breaks down the molecular mechanics of elasticity in a way that feels like a magic trick. He starts with a simple question “Why does a rubber band snap back?” and in a few short minutes, leads you through a dance of atoms and energy that’s both rigorous and joyful.

What I love about this explanation is how it embodies Feynman’s philosophy: if he couldn’t explain something in everyday terms, he didn’t believe he truly understood it. He doesn’t rely on jargon or authority; instead, he builds a mental picture so clear you feel like you’re seeing the world anew. Watching this, I’m reminded of why I fell in love with physics - it’s not just about equations but about uncovering the hidden rules that govern everyday life.

4. Orbits and Oval Paths: A Lesson in “Infinite Intelligence”

Feynman’s lecture on why planetary orbits are elliptical, beautifully reimagined by the YouTube channel Three Blue One Brown watch here, is a testament to his ability to make the complex feel elemental (but not easy). He begins with a disclaimer: “I am going to give what I will call an elementary demonstration. But elementary does not mean easy to understand. Elementary means that very little is required to know ahead of time in order to understand it, except to have an infinite amount of intelligence.”

This tongue-in-cheek remark captures Feynman’s approach perfectly. He doesn’t dumb things down; he invites you to wrestle with ideas. The lecture, animated with stunning clarity by Three Blue One Brown, walks through why orbits are oval rather than circular, using Newton’s laws and geometric reasoning. It’s a journey that requires focus - what Feynman playfully calls “infinite intelligence” - but for those who are paying close attention, rewards you with a deeper appreciation for the universe’s elegance.

For me, this lecture is a reminder that understanding doesn’t always come easily, but the effort can be worth it.

5. The Beauty of a Flower: Feynman’s Ode to Wonder

In a short but profound clip watch here, Feynman reflects on the beauty of a flower. He contrasts the artist’s appreciation of its colours and form with the scientist’s awe at its cellular structure, its evolutionary history, and the physics of its pigments. “The science,” he says, “only adds to the excitement, the mystery, and the awe of a flower.”

This moment captures Feynman’s ability to find wonder in both the simple and the complex. He didn’t see science as stripping away beauty but as revealing deeper layers of it. For him, understanding the atomic dance behind a flower’s color didn’t diminish its charm - it made it more miraculous.

This perspective has shaped how I view my work and the world. At ASML, we deal with the invisible - light, atoms, and patterns smaller than the eye can see. Yet, like Feynman, I find that the more we uncover, the more there is to marvel at. This clip is a must-watch for anyone who thinks science and beauty are at odds.

6. The Mystery of Light and Mirrors: A Question That Lingers

During my college years, a friend asked me a deceptively simple question: what’s happening at the atomic or quantum level when light reflects off a mirror? It was one of the most beautiful questions I’d ever encountered, and it sent me down a rabbit hole of exploration. Feynman’s work on quantum electrodynamics (QED), particularly his accessible book QED: The Strange Theory of Light and Matter related article here, offers a glimpse into the answer.

Feynman explains light’s reflection as a sum of probabilities, where photons take every possible path and “decide” their final trajectory through quantum interference. It’s a mind-bending concept, yet Feynman makes it feel like a detective story.

This question from college still lingers with me, a reminder of how a single curiosity can spark a lifetime of learning. Feynman’s QED lectures are a perfect starting point for anyone intrigued by the strange behaviour of light.

Why Feynman Matters Today

Feynman’s legacy isn’t just in his Nobel Prize or his contributions to physics - it’s in how he taught us to think. His insistence on first principles, his humility in the face of the unknown, and his joy in discovery are lessons for all of us, whether we are scientists, artists, or simply curious souls. In a world that often demands quick answers, Feynman reminds us to slow down, question assumptions, and build understanding from the ground up.

I encourage you to dive into these works and let Feynman’s curiosity inspire you. Whether it’s the playfulness of his rubber band explanation, the vision of “Plenty of Room at the Bottom,” or the awe of a flower’s hidden beauty, there’s something in his world for everyone.

Share your favorite Feynman moment in the comments - I’d love to hear what resonates with you!

This post was edited with the assistance of Grok 3, created by xAI.

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The Hook

Why does a Mars mission cost $74 million in one country and $671 million in another? How does a physicist’s refusal to take anything on faith reshape our understanding of the universe? And what does it take to build a chip that powers the modern world? These are the kinds of questions that keep me noodling around on my iPhone before I hit the bed - and that’s why I’m starting Climbing the Exponential.

The guy behind the keys

I’m Arvind Ravichandran, a product manager at ASML, where I work on data and diagnostics for the machines that make cutting-edge semiconductors. With a PhD in physics and a love for first-principles thinking, I naturally think deeper about the science, strategy, and ingenuity behind progress. On LinkedIn, I’ve shared reflections on everything from Richard Feynman’s clarity to India’s resourceful Mars mission. Here, I’m going deeper, exploring the forces driving semiconductors, aerospace, physics, business, tech, and AI.

Why This, Why Now?

We’re living on an exponential curve—chips are shrinking to nanoscale, space exploration is becoming accessible, and big ideas are reshaping industries. But too often, the “why” behind these leaps gets lost in technical jargon or hype. I’m starting Climbing the Exponential to cut through that noise, using my lens as a physicist and product manager to explain complex systems with clarity and curiosity. Now’s the time because the world needs more first-principles thinking—whether it’s questioning AI’s promises or understanding how constraints spark innovation.

What’s This Space About?

Climbing the Exponential is a community for anyone who wants to understand the mechanics of progress. If you’re a tech professional, a science enthusiast, or just curious about why things work the way they do, this is for you. I’ll dive into:

• The nanoscale world of semiconductors and the machines that make them.

• The business fundamentals that drive the decisions of technologists.

• How physics unlocks insights into everything from quantum mechanics to social systems.

• What innovators like ISRO or contrarian thinkers like Peter Thiel teach us about building the future.

• Why resourcefulness and clarity matter more than ever in tech, aerospace, and beyond.

This is a space to ask hard questions, challenge assumptions, and find signal in the noise. I’m not here to preach - I’m here to explore alongside you.

What to Expect?

Here’s what you’ll get with Climbing the Exponential:

• Frequency: 1–2 posts per month (800–1,500 words), blending science, strategy, and real-world insights.

• Topics: Semiconductors, physics, aerospace, business innovation, tech, and critical takes on AI. Expect posts like “How ASML’s EUV machines power Moore’s Law” or “What Feynman’s first principles mean for product management.”

• Free Subscribers: All main posts, delivered to your inbox, plus the chance to comment and join the discussion.

• Paid Subscribers (coming later): Exclusive deep dives (e.g., technical breakdowns of chip diagnostics), Q&A sessions, and early access. Paid support keeps this ad-free and rigorous.

• Schedule: No fixed days, but I’ll aim for consistency - think stories, research summaries, and practical takeaways.

My first full post will revisit Richard Feynman’s genius, exploring how his first-principles approach applies to today’s tech challenges. Subscribe to catch it in your inbox.Thanks for reading Arvind’s Substack! Subscribe for free to receive new posts and support my work.

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