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Could a fintech company be built that helps people today, and prepares them for tomorrow? One that offers consumers high quality, mobile-first credit and savings products and protects them from tricks and traps? That’s LendUp. LendUp's mission is to provide anyone with a better path to financial health. We believe there are two types of financial products: chutes and ladders. Ladders help people up, chutes push people down. One of our core values is that every product we offer at LendUp is a ladder, measured by the long-term financial well-being of our customers. LendUp credit cards, loans, and savings resources reflect our commitment to getting our customers to a better financial state. We are a data-driven company and build all of our technology in-house. With a firm belief that you're more than your credit score, we're extending credit as aggressively as possible and helping solve for the income volatility and financial instability that affects more than 140 million Americans. We are backed by more than $325 million in debt and equity financing from investors such as GV, PayPal, Y Combinator, QED Investors, Susa Ventures, Data Collective, Thomvest Ventures, Kleiner Perkins, Kapor Capital, Bronze Investments, radicle impact, Victory Park Capital, Reddit co-founder Alexis Ohanian, Gmail founder Paul Buchheit, Troy Carter, and many others. One of our values is "different backgrounds, same mission."​ We come from venerable Silicon Valley technology companies, major banks, nonprofits and law firms. We're former consumer advocates, regulators and academics. Together, we're bringing our mission to life. Join us!

Historically, quantitative models are domain specific. Brilliant people spend their best years testing features, tuning hyperparameters, and iterating architectures within a narrow domain. But scale is the panacea: large models will find patterns people, and specialized models, could not. Forecasting generalizes. Zoa trains cross-domain event forecasting engines. *Automating Iteration* LLMs, embedded in multi-agent optimization loops and evaluated against fixed policies, can automate the build-test-improve modeling cycle. Think AlphaEvolve for forecasting problems. *Sample-Efficient General Models* Today’s forecasting models are narrowly crafted with deep human priors. But larger models will outperform state-of-the-art specialized models. Unlike existing event models, our models leverage data from across contexts and rely less on human intuition. And compared to LLMs, our models are built with more inductive priors and rely more heavily on inference-time compute, improving sample efficiency. *Why It Matters* In the real economy, our models could be useful for forecasting supply chain volatility, energy supply and demand, even earthquake risk. Science is, Ian Hacking writes, the taming of chance. It is the process of iteratively updating priors (something like: identify uncertainty, conceive experiment to reduce uncertainty, execute, update). If science is uncertainty-reduction, forecasting is a critical measure of progress. Better forecasting improves our ability to select interesting experiments (roughly those with greatest expected uncertainty reduction) and update priors. Our models will be used by labs and academics in data-heavy domains. Sam's ex-girlfriend introduced him to Greg back at Carnegie Mellon in 2017, and while that relationship didn't last, their friendship has. After college, Greg went to Harvard Law School, while Sam worked for three years at Jane Street on their Options desk, building & leading a satellite dev team.