Taking time to study, build, and think. Building serious, technically complex AI systems, consulting selectively for early-stage teams, travelling, and participating in a few meaningful investment syndicates. Deliberately unhurried, choosing depth over volume.
Sole business leader at the Arbitrum Foundation, reporting directly to the board. The only person with full operational authority across the organisation — no playbook, no precedent, no team inherited. Built the foundation's institutional identity from zero: wrote the constitution, mission, and governance framework; designed the accountability structures; established legal and compliance infrastructure across multiple jurisdictions.
Designed and ran the foundation's strategic programmes: the grant programme, the global ambassador programme, educational initiatives, and ecosystem partnerships. Led global expansion across Asia, Europe, and the Americas. Managed external service providers across legal, compliance, recruitment, and country operations. Arbitrum was a decentralised autonomous organisation, which meant governance was not theoretical — every decision required navigating on-chain governance, token holder accountability, and the tension between decentralised legitimacy and operational execution. That practical experience running governance at scale is what I carry into any foundation or institutional context.
Joined as an early non-technical employee and did everything it took to build the ecosystem. No fixed job description — business development, grants, hiring, product consultation, partnerships, enterprise onboarding, all of it simultaneously. Helped take Polygon from early stage to the number one blockchain ecosystem globally in under 14 months.
Consulted Fortune 500 companies and internet-scale businesses on blockchain adoption end to end: identifying the right use case, designing the product approach, and seeing it through to execution. These were not introductory conversations — they were full sales and strategy cycles with senior stakeholders at companies that had no prior blockchain exposure. Also ran the grants programme, supported thousands of founders, and led hiring across functions as the ecosystem scaled.
Helped build and ship AI features for GEP's contracts-management module, including OCR pipelines, key-term extraction, and risk analysis workflows. Contributed across the full product cycle from research and PRDs to prototyping and testing. Received an early promotion for research depth and product thinking.
The Consilience is research infrastructure for the hypothesis layer of scientific discovery — the moment before the experiment, when a structural correspondence across disciplines is recognised but not yet formalised. Built on a three-level embedding architecture, hybrid retrieval (BM25, dense vector, knowledge graph, reranking), and a grounded-generation contract: every output is cited from your own ingested sources only, never from the internet. Current focus: quantum physics and its structural parallels with contemplative science. Roadmap: neuroscience, domain-specific SLM trained from the knowledge graph, and foundational architecture research into structural correspondence beyond similarity.
Consilience also publishes research and essays at theconsilience.io/intelligence-was-here-before-we-were and theconsilience.io/after-the-transformer.
The original description: Consilience is a personal knowledge engine for serious thinkers, researchers, and
writers who work across disciplines, traditions, and subjects. It captures your
thinking via voice, ingests your sources: PDFs, YouTube, articles, books, and people
by name, and builds a private knowledge graph that connects your ideas, your sources,
and thinkers whose work converges with yours from completely different directions.
Your lens, calibrated from your own recognition language, vocabulary, and worldview,
becomes the translation layer through which every source is read. When you write,
Consilience retrieves from this library through your lens and generates in multiple modes:
Parallel Lens, Gap Finder, Synthesis, Essay and Book Chapter, and Compare. It suggests
thinkers you have never encountered whose essence-level thinking maps to yours, not
by keyword but by recognition. The longer you use it, the more precisely it sounds
like you, draws the connections you would draw, and surfaces the dots you would
connect. Because it is not a generic AI assistant. It is your knowledge, your lens,
your library, working together.
theconsilience.io →
A production-grade nine-node multi-agent AI workflow for insurance claims processing, built in four hours as a forward-deployed prototype. The system handles end-to-end claims triage: email ingestion, parallel data retrieval across customer records, policy contracts and communication templates, AI-driven underwriting reasoning, human-in-the-loop approval before any communication sends, and automated customer notification with a full timestamped audit trail on every node transition for regulatory compliance. Designed to demonstrate how regulated industries, insurance, mortgage, healthcare, financial services, can transform high-volume human judgment workflows into auditable, compliant AI systems without sacrificing oversight. The architecture generalises: the same orchestration pattern applies to mortgage underwriting, healthcare prior authorisations, and loan origination.
github.com/Anuja-Khatri/insurance-claims-ai-agent →
A platform to move liquidity between any vault or protocol with a single click, abstracting cross-chain complexity. Prototyped ML-based recommendations and self-executing transactions. Paused. Not because the idea failed, but because I wasn't willing to build for the narrative or the funding round.
AI architecture and research
How I build with AI
Strategy and operations
Education
Intelligence Was Here Before We Were
On cognition, consciousness, and what the architecture of mind tells us about the architecture of machines. The universe is 13.8 billion years old. The transformer architecture was published in 2017. In between, intelligence took forms we are only beginning to understand. This essay traces what was happening before cognition, why the brain is not one thing, and what the greatest scientists found when they went deep enough into the structure of physical reality.
After the Transformer
AI solved an 80-year-old mathematics conjecture. It found 31 new planets. Here is what that actually means — and where the real gap in scientific discovery lives. A survey of every serious attempt to go beyond next-token prediction: neurosymbolic AI, verifier-centric systems, world models, causal representation learning, GFlowNets, and structure mapping. Each approach mapped honestly, including where it stops. Closes with why the recognition layer of discovery — the moment before the hypothesis — has no infrastructure, and what that means.
The Legitimacy Gap at the Heart of AI Alignment
AI alignment has two problems. The technical one gets all the attention. The governance one is almost entirely ignored, and it is the prior problem. Whose values? Decided how? Accountable to whom? You cannot get the specification right if you have not answered who gets to specify in the first place. The essay walks through what every major approach actually does and where it breaks. RLHF, Constitutional AI, OpenAI's model spec, the EU AI Act: each one assumes the specification is legitimate. None of them have a process for making it so. Iason Gabriel at DeepMind, Gillian Hadfield at Toronto, and Atoosa Kasirzadeh at Edinburgh are all pointing at the same gap. The process is broken before the content is even written. Three things are missing from every current approach. Legitimacy: no AI lab has a process for deciding values that any affected party participated in. Revisability: specifications can be changed by the company tomorrow with no external participation, no delay, no public record. Accountability: when things go wrong, the response is a blog post. Four technical architectures make the governance framework real rather than aspirational. Hardware-walled computation regions where certain pathways were never built, not trained against. A live metacognitive register that tracks internal states automatically before they drive outputs. Formal mathematical verification of bounded safety properties, stronger than probabilistic testing. And cryptographic proofs that let regulators verify compliance without seeing model weights. Each governance layer has a technical equivalent. The constitutional layer needs hardware where violations are physically impossible. The accountability layer needs cryptographic verification that removes the need for trust. The revisability layer needs a live internal dashboard that catches failures before they reach outputs. Without the technical layer, governance is aspiration. With it, governance is verifiable. It closes with the question nobody in AI governance is asking: what should the deliberation process itself be checked against, other than more human preferences?
Intelligence Is Not a Scale: A Structural Taxonomy for Evaluating Artificial Minds
The debate about whether AI systems understand, feel, or deserve moral consideration has stalled because everyone is arguing on the wrong axis. This paper proposes that intelligence is not a single scale but a layered structure with three qualitatively distinct levels, each requiring conditions the level below cannot produce through scaling alone. A brainless single-celled organism satisfies all ten conditions of the second layer. The world's most capable language models satisfy none. The paper closes with three architectural frameworks specifying what crossing this gap would actually require.
Tat Tvam Asi: Integrating Vedic Ontology and Modern Science in the Study of Consciousness
This paper proposes an integrative model of consciousness bridging Vedic metaphysics, modern physics, neuroscience, and evolutionary biology through a single organising principle: coherence. It argues that quantum field theory and Vedic descriptions of Brahman are converging on the same reality, and that consciousness is not an epiphenomenon of matter but the coherence field that organises energy into form. Submitted to the World Association for Vedic Studies, WAVES 2026.
Intelligence Beyond Representation: What AGI Misses When Language Becomes the Substrate
Current AI development implicitly assumes that intelligence is a function of representations, symbolic rules, embeddings, or language models. This essay challenges that by examining intelligence through evolution, neuroscience, biology, and physics. Drawing on LeCun, Hoffman, Friston, and Levin, it proposes a three-layer model, brain, mind, consciousness, and argues that LLMs occupy only the first layer. The architectural implications for AGI are concrete.
The Myth of Fully Decentralized Governance
Written from inside one of Web3's largest DAOs, this essay challenges the ideology of full decentralization as a governance model. Drawing on first-hand experience at Arbitrum and case studies across Compound and Solana, it argues that DAOs fail not because of their technology but because of the absence of structure, leadership, and aligned incentives. The essay proposes hybrid governance as the practical path forward: decentralize execution and transparency, but keep vision and strategic decision-making in the hands of committed, qualified people.
I grew up in a household where ambition had to be earned, not assumed. I chose Physics, with Mathematics and Statistics, not because it was practical, but because I needed to understand how things actually work underneath. That instinct shaped everything that followed. Before my MBA, I worked as a cold caller to fund it. That job taught me more than most classrooms did: how to earn attention in one sentence, how to stay grounded after rejection, and how to hear what people mean rather than what they say.
The pattern in my career is not an industry. It is depth of comprehension, applied fast. At GEP I was working across pharma clients like Roche and enterprise contracts, learning compliance and how large organisations make decisions. At Polygon I was distributing grants to founders across automobile, energy, real estate, and finance simultaneously, which meant understanding what each industry was actually trying to solve before deciding who deserved capital. I moved from enterprise software to blockchain with no roadmap and reached an executive seat at one of the largest foundations in the space within three years. I write research papers on AI safety, consciousness, and DAO governance, not as a side interest but because when I pick something up, I go all the way in. I am now building production AI systems. The through-line is not hustle. It is that I find the underlying structure of whatever I enter, quickly, and operate from there. That is what I do in every room I walk into.
My path has never moved in a straight line, and I think that is the point. Physics taught me to model systems. Six Sigma taught me to eliminate what doesn't matter. An MBA in strategy gave me the language for decisions at scale. Working in AI product management, then deep-tech ecosystems, then running operations for a foundation managing billions, each chapter came with a steep learning curve I had no choice but to climb. I have never waited to feel ready. I have always figured it out.
I have always been drawn to things before they were obvious, early-stage, ambiguous, real ownership. I turned down a Google offer to go deeper into something I believed in more. I do my best work when the map does not exist yet, the team has high integrity, and the problem genuinely matters. I also attempted to build a DeFi infrastructure startup and chose to pause it. Not because the idea failed, but because I was not willing to build something just for the narrative or the funding round. That distinction matters to me more than momentum for its own sake.
I have travelled solo across 10+ countries. Outside of work, I read across physics, ancient scriptures, philosophy, AI and consciousness, not as separate subjects but as different angles on the same question. Spirituality, to me, is higher-order logic: a framework for understanding reality that predates and often outreaches what modern science has caught up to yet. I prefer depth over breadth, in ideas, in work, and in people.
Away from screens I keep fish, tend to plants, and spend time around animals and nature. I play table tennis and lift weights. These are not hobbies so much as the counterweight that keeps everything else honest.
On stage
On the mic
KryptoSeoul
Metaverse Summit · Paris
Talks & panels
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