New Infrastructure Primitives Enable Programmable Money, Autonomous Science, AI Distribution

Key Quotes

"I think a lot of the existing stable coins look effectively like narrow banks today where they hold user deposits in fiat or perhaps in treasury bills I think it's very unlikely in the long term that we scale on chain finance exclusively through narrow banks and so i've been thinking much more about how we can facilitate credit and capital formation on chain"

Guy Withitt argues that current stablecoin models resemble narrow banks, which hold deposits but do not actively facilitate credit. He suggests that scaling on-chain finance will require moving beyond this model to actively enable credit and capital formation directly on the blockchain.

"how we originate credit natively on chain and i think this is important specifically because it can drastically reduce back office costs like loan servicing which in many cases can take one to 3 of the outstanding credit facility itself every year so it's incredibly expensive but i also think doing on chain origination will allow for much more composability between different defi projects"

Guy Withitt highlights the cost savings and increased composability that can be achieved by originating credit directly on-chain. He explains that this approach can significantly reduce expensive back-office operations like loan servicing and enable greater integration between different decentralized finance (DeFi) projects.

"what is new and what is emerging right now is the combination of reasoning capabilities and experiment planning and the physical element of lab automation so what that might look like in the near term is collaboration between a scientist and a system that involves both an ai application and a robot and having that be a much more collaborative process in the near term"

Oliver Shu explains that the novelty in scientific research lies in combining AI's reasoning and planning abilities with physical lab automation. He envisions a near-term future where scientists collaborate with AI systems and robots, making the research process more interactive and efficient.

"one of the things that i think is important in the near term though is around interpretability so you know if you think about ai systems as non deterministic computers one of the things that really matters for research is you want to really understand why the system is doing what it's doing why it's planning on iterating on an experiment in a given way"

Oliver Shu emphasizes the critical role of interpretability in the development of autonomous labs. He notes that for AI systems used in research, understanding the "why" behind their actions and experimental plans is essential, especially given their non-deterministic nature.

"one of the most powerful and underrated ways for startups to win distribution is actually to serve companies at formation or greenfield companies the battle between every startup and incumbent ultimately comes down to whether the startup can get to distribution before the incumbent innovates"

James da Costa proposes that a powerful strategy for startups to achieve distribution is by serving other companies at their formation stage, referred to as "greenfield companies." He posits that the core competition between startups and established incumbents hinges on a startup's ability to secure distribution before incumbents can innovate.

"if you attract all of the new companies at formation and then grow with them as your customers become big companies in their own right so will you this is actually the playbook that stripe used over a decade ago many of stripe's first customers did not exist when stripe was founded but as stripe's customers grew to become big customers in their own right stripe used them as a case study to then sell to other enterprises outside of silicon valley"

James da Costa illustrates the "greenfield strategy" by referencing Stripe's early success. He explains that by serving nascent companies and growing alongside them as they become large enterprises, a startup can achieve significant scale, using their initial customers as a foundation for broader market penetration.