An In-Depth Interview-Style Profile on Research, Entrepreneurship, and the Future of AI
In the rapidly evolving fields of artificial intelligence and machine learning, a small number of individuals manage to operate effectively at the intersection of deep technical research and large-scale real-world implementation. Neel Somani is one of those individuals. As a researcher, entrepreneur, and founder, Somani has built a reputation for combining mathematical rigor, engineering discipline, and long-term vision. His work spans artificial intelligence, mechanistic interpretability, reinforcement learning, and blockchain infrastructure, positioning him as a key figure shaping how advanced systems are built, understood, and scaled.
Somani is best known as the founder of Eclipse, a blockchain platform that has drawn widespread attention for its speed, scalability, and architectural innovation. Eclipse leverages the Solana Virtual Machine to create what is widely recognized as Ethereum’s fastest Layer 2 platform. The project’s success is reflected not only in its technical achievements but also in its market validation, including $50 million raised in Series A funding. Yet Somani’s work extends well beyond blockchain. His research contributions in artificial intelligence, particularly in interpretability and large language models, demonstrate a consistent focus on making complex systems more understandable, reliable, and useful.
This article examines Neel Somani’s background, professional development, major achievements, current research focus, and long-term vision. Through an interview-style narrative, it presents a clear picture of how Somani approaches innovation and why his work matters in the broader AI and machine learning ecosystem.
Neel Somani’s academic background provides important context for understanding his approach to problem-solving. He earned a triple major in computer science, mathematics, and business administration from the University of California, Berkeley. This combination is notable not simply for its difficulty, but for how directly it reflects his interdisciplinary mindset. Each discipline plays a distinct role in his work: computer science for system design, mathematics for formal reasoning and proofs, and business for understanding incentives, scalability, and real-world deployment.
During his time at UC Berkeley, Somani was actively involved in research that emphasized correctness, privacy, and formal guarantees. One of the most significant projects he contributed to was Duet, a formal verifier designed to prove whether code is privacy-preserving under differential privacy constraints. This work required a strong grasp of theoretical computer science and mathematical logic, as well as practical programming skills.
Somani’s experience with Duet helped shape his long-standing interest in interpretability and safety. Rather than treating AI systems as opaque black boxes, he has consistently pursued methods that allow researchers and engineers to reason formally about what systems are doing and why. This early exposure to formal verification would later influence his research in mechanistic interpretability and his broader philosophy around trustworthy AI.
After completing his studies at Berkeley, Somani joined Citadel as a quantitative researcher in the commodities group. In this role, he worked on complex market models that required precision, speed, and an ability to reason under uncertainty. Quantitative finance is an environment where small errors can have large consequences, and this setting further sharpened Somani’s analytical discipline.
At Citadel, Somani was exposed to large-scale data systems, high-performance computing, and decision-making frameworks that balance theoretical models with empirical performance. This experience reinforced the importance of building systems that not only work in theory but also perform reliably in production environments.
Although his time in finance was relatively brief compared to his later entrepreneurial work, it played a critical role in shaping his perspective. The emphasis on optimization, feedback loops, and real-time decision-making would later reappear in his research on reinforcement learning and AI training dynamics.
Somani’s career took a decisive turn with the founding of Eclipse. The project represents a synthesis of his technical expertise and his interest in building scalable, high-performance systems. Eclipse is designed as a Layer 2 solution for Ethereum, leveraging the Solana Virtual Machine to achieve significantly higher throughput and lower latency than traditional approaches.
Under Somani’s leadership, Eclipse quickly gained recognition for its technical ambition and execution. The platform demonstrated that it was possible to combine Ethereum’s security and ecosystem with Solana’s performance-oriented execution model. This hybrid approach addressed long-standing bottlenecks in blockchain scalability, positioning Eclipse as a leading solution in the Layer 2 space.
The market response to Eclipse was strong. The company secured $50 million in Series A funding, signaling confidence from investors in both the technology and Somani’s vision. More importantly, Eclipse showcased Somani’s ability to translate complex engineering ideas into systems that attract real adoption and capital.
While blockchain infrastructure may seem distant from AI research, Somani views the two domains as complementary. Both require careful system design, an understanding of incentives, and robust methods for handling scale and complexity. His work at Eclipse reflects these shared principles.
In parallel with his entrepreneurial efforts, Somani has made significant contributions to artificial intelligence research. One of his recent projects is Symbolic Circuit Distillation, an extension of OpenAI’s Sparse Circuits work. This research focuses on understanding large language models by automatically identifying and extracted circuits responsible for particular behaviors.
The core idea behind Sparse Circuits is to make large models more interpretable by isolating the relevant components involved in answering a given query. Instead of treating a language model as an indivisible whole, the method seeks to map its internal computations to simpler, more human-understandable representations. Symbolic Circuit Distillation builds on this work by automatically finding those human-understandable representations, and proving that they are correct.
This work addresses one of the central challenges in modern AI: interpretability. As models grow larger and more capable, understanding why they behave the way they do becomes increasingly difficult. Somani’s research contributes tools and frameworks that help bridge this gap, enabling researchers to reason about model behavior with greater clarity.
Importantly, Symbolic Circuit Distillation is not purely theoretical. It is designed with practical applications in mind, offering insights that can inform model debugging, safety analysis, and alignment efforts. This balance between theory and practice is a recurring theme in Somani’s work.
At present, Somani is deeply focused on advancing mechanistic interpretability and reinforcement learning as applied to large language model training. He is particularly interested in how models learn from feedback and how those learning processes can be improved to more closely resemble human learning.
One area of emphasis is reinforcement learning from feedback, where models are trained not just on static datasets but on iterative signals that guide behavior over time. Somani believes that refining these methods is essential for building AI systems that are adaptable, reliable, and aligned with human intent.
Another key focus is memory and information retrieval. Current language models are limited in how much information they can recall and reason over at once. Somani is exploring approaches that could allow AI systems to access and utilize vastly larger knowledge stores, potentially spanning entire libraries or databases.
These efforts are closely tied to his interpretability work. By understanding how models store, retrieve, and manipulate information internally, researchers can design systems that are both more capable and more transparent.
Looking ahead, Somani envisions a future in which artificial intelligence systems are far more unified across modalities. He anticipates breakthroughs that reconcile techniques across language, images, and video, enabling models to reason seamlessly across different forms of data.
Another major area of interest is the de-compilation of Transformer models into human-readable programs. This line of research aims to translate the internal workings of neural networks into symbolic representations that humans can inspect and understand. If successful, it could fundamentally change how AI systems are audited, debugged, and governed.
Somani also places strong emphasis on formal methods in AI research. He believes that as AI systems become more powerful, the need for rigorous guarantees around safety, robustness, and correctness will only grow. His background in formal verification positions him well to contribute to this emerging area.
Beyond his technical work, Somani has demonstrated a commitment to supporting future generations of researchers and engineers. Through a personal scholarship program, he provides support for higher education, reflecting his belief in the importance of access to learning opportunities.
This commitment aligns with his broader philosophy. Somani does not view innovation as a solitary pursuit, but as a collective effort that benefits from diverse perspectives and sustained investment in talent.
Neel Somani’s career illustrates what is possible when deep technical knowledge is combined with entrepreneurial drive and long-term vision. From his academic work in formal verification to his research in AI interpretability and his leadership at Eclipse, Somani has consistently focused on making complex systems more efficient, understandable, and impactful.
His contributions to artificial intelligence and machine learning are both substantial and forward-looking. By bridging theory and practice, and by working across domains that are often treated separately, Somani is helping to shape the future of intelligent systems. As AI continues to transform industries and societies, his work stands as a model for how innovation can be both ambitious and responsible.
