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Liz Harkavy
Krypto @a16z | Har arbetat på @Facebook, @NASAJPL, @GoldmanSachs | Fysik och datavetenskap @MIT Meng EECS @MIT ||
Förra veckan var en historisk vecka för krypto och för USA med antagandet av stablecoin-lagstiftningen, en av bara några få större finansiella lagar som undertecknats under de senaste 25 åren. Det markerar ett meningsfullt steg mot att göra USA till hemmet för krypto.
Idag är vi glada över att kunna tillkännage vår såddinvestering i @CowrieIO, ett rådgivningsföretag baserat i Wyoming som specialiserar sig på inhemsk skatteefterlevnad och enhetsstrukturering. Vi tror att USA är på väg att anta omfattande kryptolagstiftning, och @DKerr_Cowrie & Cowrie är väl positionerade för att hjälpa DAO:er och kryptoprojekt att uppfylla sina regulatoriska skyldigheter.
Läs mer om Cowrie och vår vision för framtiden för kryptoföretag och stiftelser nedan.
27,02K
Jag får en konstig glädje av att berätta för AI-modeller att de har fel. Det visar sig att instinkt faktiskt är värdefull – mänsklig feedback är det som gör AI-modeller bättre, och @pankaj och Yupp-teamet har byggt den perfekta plattformen för att utnyttja den.
Jag är så glad över att stödja @yupp_ai när de bygger en öppen infrastruktur för utvärdering av AI-modeller.

Chris Dixon14 juni 2025
I’m excited to announce we’ve led a $33 million seed round in @yupp_ai, a consumer product that allows anyone to discover and compare the latest AI models for free. AI needs robust and trustworthy human data. Crypto is built to provide it.
Modern AI systems are shaped not only by compute and algorithms but by human feedback. Companies use post-training techniques such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimisation (DPO) to improve their models. These techniques can reduce bias and enable higher quality, more coherent responses to prompts — crucial for accelerating progress in AI. Model evaluation is similarly critical, but a model can only be made better after first deciding what “better” means.
That’s where challenges arise: Companies don't like to share — they keep their data and training processes secret. As a result, model improvements are constrained by what can be learned from closed systems or static benchmarks that are rarely informed by real-world use. These constraints make AI models difficult to evaluate. Users are also left in the dark, with little insight into how their feedback shapes models or whether it’s used at all. Some leaderboards and crowdsourcing sites attempt to shed light here, but they generally don’t enable users to audit their contributions or see any direct benefit from participating. Platforms that claim to be fair and transparent often rely more on good faith than enforceable standards.
We believe crypto can bring transparency and ownership to this murky area of AI. Blockchains can make it easier for people to receive rewards for their contributions. They can also provide AI builders with assurances about the quality and provenance of the feedback data and evaluations they’re incorporating into their models. So users get incentives, builders get trustworthy data, and everyone can audit either side of the open market.
Yupp crowdsources model evaluation: users enter prompts, see multiple AI-generated responses side-by-side, and then pick the best ones. Their choices create digitally signed “packets” of preference data that are useful for AI post-training and evaluation. In addition to users getting access to the latest models for free, they receive rewards based on the feedback that they provide.
Yupp’s design turns human judgment into a renewable economic resource. Data “expires” as newer interactions replace it, creating a natural flywheel: more usage yields fresher evaluations; fresher evaluations yield better models; better models attract more usage. All participants — from users to AI model builders — can participate and see that the same transparent rules apply to everyone, ensuring a credibly neutral marketplace. No one can hide the scoreboard, and no one can manipulate the rewards or results.
The founders bring deep experience in both AI and crypto. They built consumer-scale machine learning products together in the early days of Twitter. @pankaj ran global consumer engineering for Google Pay and @Coinbase. @gilad was a machine learning lead at GoogleX. The early team already counts senior engineers from Google, Coinbase, and top research labs.
AI needs strong, reliable evaluation based on large-scale human input. Crypto is the trust machine that can help deliver it. By enabling people worldwide to contribute model-improving feedback, Yupp aims to become the default evaluation layer for the future of AI. We’re proud to back Yupp and look forward to helping them build the onchain feedback loop that ensures the rewards of AI innovation are shared by everyone who helps create it.

2,9K
AI-agenter bryter ekonomin på den öppna webben. Innehållssajter förlorar trafik, betalväggar ökar och agenter som integrerar med dataleverantörer existerar till stor del i informationssilos.
Men tänk om vi kunde bygga in intäktsdelning direkt i internets arkitektur?
Läs om detta tillsammans med andra användningsfall som vårt team har tänkt på nedan

6,26K
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