Darshan
7.3K posts

Darshan
@DarshanG_
research & investments @polarisfund | excited about web3, ai & frontier tech





I spent the last few months writing a 4000 word thesis on "crypto neobanks" - looking at neobank history, market landscape, revenue models, and gaps for builders. Here's a TLDR: - The first wave of fintech neobanks (SoFi, Chime, Revolut, Wise) happened in the mobile era. They replaced the frontend of banking from a physical branch to a mobile app. Crypto neobanks are replacing the backend of banking - from traditional rails to permissionless ones. - Crypto neobanks - like their fintech predecessors - address 4 fundamental interactions with money: Store, Spend, Grow, and Borrow. Incumbents at every layer (Phantom/EtherFi/Binance/Morpho) are racing to consolidate these features into a single "superapp" experience to increase user stickiness. - The core goal of a crypto neobank is to make money move around faster, using blockchain rails. Some verticals accelerate money more than others, and each has a different monetization model. There's a velocity of money pyramid: Grow > Borrow > Spend > Store. - Opportunities for builders: (1) Privacy and compliance parity with existing banks/fintechs (2) Real world composability (merchant systems, SWIFT, local rails like ACH/Pix) (3) Leveraging permissionlessness nature of blockchain rails to speed up how money is transferred in the economy, for rich, poor, human, and AIs, (4) Localization vs. globalization - crypto neobanks can pursue both paths, either with a local-first (Nubank style) model or a global-first model. The former wins through local trust and distribution, the latter through scale and composability. (5) Undercollateralized lending and consumer credit - this is perhaps the “holy grail” of crypto neobanks, combining compliance/KYC, bridging offchain credit records with onchain ones, and navigating regional differences.








Prediction: Monad and Berachain will be instant flops






Someone needs to build a company around Customer Context Graph. Collect all the threads – emails, meeting transcripts, slack messages, contracts, deliverables, detail, info, and config – from your customers into context that can be explored and queried by agents. This info is scattered between CRMs, ticketing systems, note takers, product, landing pages – it's inherently cross platform information. You need a new solution. Kind of how Segment did it trad SaaS apps. With this context, you can fire up Claude Cowork or similar for ad-hoc work or build extremely powerful agent automation flows. Expose the context as skills, MCP, and file system. Even better if you build it as open-source with a hosted option so people can take it on-prem as needed. Create a connector ecosystem around it. This will power every single next-gen AI-native full-stack business. Sort of like the context graph (@ashugarg @JayaGup10 ) that has been discussed recently but I'm thinking something very concrete: "Get me all the context about this particular customer." A customer-level, cross-system context substrate that agents can explore and act on








