Pagaya vs Thought Machine

Thought Machine has a disclosed valuation of $2.7B, while Pagaya's valuation is not publicly available, making a direct size comparison difficult. Thought Machine employs 500-1000 people.

Pagaya has raised more in total funding at $600M, compared to Thought Machine's $563M — a gap of $37M. Combined, the two companies have completed 3 known funding rounds.

Thought Machine leads with an Awaira Score of 85/100, while Pagaya sits at 70/100. That 15-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Pagaya was founded by Gal Krubiner in 2016. Thought Machine was founded by Paul Taylor in 2014. Visit each company's profile on Awaira for a full founder biography.

Pagaya: Pagaya operates an AI financial underwriting network that processes consumer loan applications on behalf of lenders, using machine learning models that evaluate creditworthiness across a broader set of data signals than traditional credit bureau scores, enabling lenders to approve more applicants while maintaining or improving default rates. The Tel Aviv and New York company monetises by taking a network fee on loan volume processed through its AI underwriting system, funded by institutional investors who purchase the approved loan pools.\n\nThe company went public on NASDAQ via SPAC merger, having raised over $600 million in combined public and private funding from investors including Oak HC/FT and Viola Growth. Pagaya reports processing hundreds of billions of dollars in loan applications annually across personal loans, auto loans, and mortgage products, with network partners including SoFi, Ally Financial, and US Bank embedded in its origination technology. The business model operates as an AI network sitting between lenders who originate applications and institutional investors who fund approved loans.\n\nPageya competes in the AI credit underwriting market against ZestFinance, Upstart, and traditional credit bureau scoring models from Fair Isaac. Its network model, where multiple lenders access the same AI infrastructure and their collective data improves model performance over time, creates compounding advantages compared to single-lender AI implementations. The company has navigated regulatory scrutiny around AI lending decisions and disparate impact as financial regulators increase oversight of alternative data use in credit decisions. Thought Machine: Thought Machine builds Vault, a cloud-native core banking platform that uses a smart contract programming language to define financial products as configurable code rather than hardcoded legacy software. The London company targets tier-one and tier-two banks seeking to replace decades-old mainframe core banking systems with a modern, API-first platform that can deploy on any major cloud provider and enable rapid product iteration.\n\nThe company raised approximately $563 million including a $160 million Series D led by Temasek, with Intesa Sanpaolo and Standard Chartered among its strategic investors, giving it a valuation of approximately $2.7 billion. Thought Machine counts Lloyds Bank, Curve, SEB, and JPMorgan among its enterprise clients, with live deployments processing significant volumes of retail and commercial banking transactions on its cloud-native infrastructure.\n\nThought Machine operates in a core banking replacement market estimated at over $10 billion annually, competing with Temenos, Finacle, and Mambu for bank transformation budgets. Its product architecture is considered technically differentiated by banking analysts, as the smart contract approach to product configuration allows banks to define entirely novel financial products without custom development work. The company faces the long sales cycles and regulatory validation requirements inherent in replacing critical banking infrastructure, but its growing client roster of established financial institutions validates its position as a credible replacement for legacy core banking vendors.

Thought Machine got there first, launching in 2014 — that's 2 years of extra runway. Pagaya didn't arrive until 2016. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Both Pagaya and Thought Machine report about 500-1000 employees. Team size is a rough proxy for scale, but lean AI companies routinely punch above their headcount.

Yes — they're direct rivals. Both Pagaya and Thought Machine compete in AI Finance, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.