22 23 Finance Monthly. Finance Monthly. Beyond the Deal Beyond the Deal In an exclusive conversation with cofounder Sara Hooker, a leading voice in modern AI research, Finance Monthly explores the strategy behind the raise and the company’s ambition to move the industry beyond the “bigger is better” paradigm. We examine how the financing was executed, the technical philosophy driving Adaption Labs, and what the transaction signals for the next phase of enterprise AI investment. You’ve raised $50 million to challenge the “bigger is better” dogma. Why is the industry at a “reckoning point” regarding model size? Most labs won’t quadruple the size of their model each year, mainly because we’re seeing saturation in the architecture. We are moving away from it just being a model. This is part of the profound notion—it’s based on the interaction, and a model should change in real time based on what the task is. The deal highlights your focus on “gradient-free learning.” How does this change the unit economics for an enterprise? The most costly compute is pretraining compute, largely because it is a massive amount of compute and a massive amount of time. With inference compute, you get way more bang for each unit of power. How do you update a model without touching the weights? There’s really interesting innovation in the architecture space, and it’s leveraging compute in a much more efficient way. You’ve been vocal about eliminating “prompt acrobatics.” What is the goal for the end-user experience? My goal is to eliminate prompt engineering. Most enterprises currently use extensive prompt and context engineering to adapt models, but these prompts often stop working when a new ADAPTION LABS SECURES $50M SEED TO REDEFINE ENTERPRISE AI ECONOMICS version of the model is released. I want to create models that learn continuously without expensive retraining or finetuning. Adaption Labs is built on three “adaptive pillars.” How do these differentiate you from the frontier labs? Proving that AI can efficiently learn from an environment will completely change the dynamics of who gets to control and shape AI. Our pillars—adaptive data, adaptive intelligence, and adaptive interfaces—couldn’t be achieved within a traditional frontier lab because those areas are split across different teams. This is probably the most important problem that I’ve worked on. You’ve expressed skepticism regarding how current models handle hardware. How does that shape your work with Sudip Roy? Ideas in AI often succeed or fail based on whether they happen to fit existing hardware, rather than their inherent merit. My cofounder makes GPUs go extremely fast, which is important for us because of the real-time component. Sector: Enterprise AI / Foundation Models Transaction Type: Seed Growth Financing Deal Size: $50 million Lead Investor: Emergence Capital Partners Deal Status: Completed Completion Date: February 2026 INSIDE THE STRATEGY WITH SARA HOOKER In February 2026, San Francisco–based Adaption Labs closed a $50 million seed round, led by Emergence Capital Partners. The financing marks one of the largest seed commitments in the “neolab” sector, featuring participation from Mozilla Ventures, Fifty Years, Threshold Ventures, Alpha Intelligence Capital, E14 Fund, and Neo. The deal signals a strategic pivot in AI investment, moving away from the “scaling laws” that defined the OpenAI era. Adaption Labs, founded by former Cohere executives Sara Hooker and Sudip Roy, is focused on “gradient-free” learning—a method that allows models to adapt in real-time without the multi-million dollar costs of traditional retraining. While the company declined to disclose its post-money valuation, the scale of the seed round positions Adaption Labs as a primary challenger to the industry’s reliance on massive, static LLMs. Execution and Technical Philosophy The transaction reflects investor confidence in Adaption’s ability to solve the “continuous learning” problem. Traditional models are “frozen” after training; Adaption’s systems use dynamic decoding and on-the-fly merging to select specific “adapters” based on a user’s query. • Adaptive Data: Systems generate and manipulate the data they need to answer a problem on the fly, rather than relying on large static datasets. • Adaptive Intelligence: Models automatically adjust how much compute to spend based on the difficulty of the task. • Adaptive Interfaces: The system evolves based on how users interact with the system, moving beyond the standard “chat bar.” What the Deal Signals The success of the Adaption Labs raise suggests that the “Hardware Lottery”—a concept Hooker pioneered— is entering a new phase. Capital is now flowing toward efficiency and “inference-time” intelligence rather than raw horsepower. This deal underscores a vital trend: the next generation of AI value will be found in ownership and control. By lowering the cost of adaptation, Adaption Labs aims to allow companies to “shape and own” their AI rather than renting a generic, static model from a centralized provider. Corporate Insight: Beyond the Lab Adaption Labs operates as a multi-disciplinary architect of transformative solutions, positioning itself at the intersection of productivity and financial intelligence. While the core laboratory focuses on frontier AI research, the company’s broader ecosystem delivers tools designed for immediate executive decision-making. Key specialized offerings include: • Humility: A mobile application built for fundamental investors. It enables users to uncover and refine the intrinsic value of company shares, facilitating high conviction investment choices in a volatile market. • Risk Aversion: A precision tool for value investors that provides company-specific checklists and customizable risk factor templates. This allows for rigorous, audit-ready research and clear buy/sell recommendations. The firm’s philosophy emphasizes a seamless fusion of simplicity and design, ensuring that complex backend innovations remain accessible to the end-user. By fostering a collaborative ecosystem, Adaption Labs aims to move technology from a static service to a dynamic partner in enterprise growth. Deal Overview
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