5Y News | AI-Native Investment Management Firm GIM Closes Tens of Millions of Dollars in Series A Funding

Renaissance Technologies Co-Founder Leads Investment as GIM Closes Series A

AI-native intelligent asset management company GIM recently completed a Series A round of tens of millions of dollars, co-led by Jinyong Capital (a vehicle under HONY Capital) and B Capital. IDG Capital and existing investor Monolith also participated. Howard Morgan, B Capital's current Global Chairman, is co-founder of quantitative investing giant Renaissance Technologies. Zhiguan Capital served as exclusive financial advisor for this and all subsequent rounds.

This marks GIM's third funding round in six months since its founding last July, following its angel round (Monolith, 5Y Capital) and angel+ round (led by SAIF Partners). The investor roster spanning quantitative investing legends, top-tier VCs, and leading industry capital reflects the market's intense interest in the AI-native asset management paradigm.

Following this round, GIM will continue advancing the coordinated development of financial large language models, self-evolving agent systems, and asset management products — further expanding AI's application boundaries across investment research, strategy development, and asset management.

Why Finance, Why Now

As large model competition shifts from general capabilities toward specialized verticals, finance is emerging as one of the few AI landing zones that combines high value with high barriers to entry. Unlike standardized tasks such as customer service, marketing, or office automation, asset management confronts a complex system shaped by numerical reasoning, temporal dynamics, risk constraints, and dynamic feedback. AI that genuinely penetrates this domain cannot merely organize documents or generate summaries — it must constitute a system capable of sustained research, forming judgments, and submitting to market validation.

This is precisely where GIM has positioned itself.

Two Pillars: Vertical Domain LLMs + Self-Evolving Agents

The company is currently advancing along two parallel tracks toward next-generation asset management infrastructure: a vertical large model purpose-built for financial scenarios, and a self-evolving agent system structured around the investment research workflow. The former addresses financial data comprehension, temporal modeling, and reasoning capabilities; the latter addresses how research processes can be decomposed, coordinated, evaluated, and continuously optimized. These two tracks advance in parallel, forming GIM's core thesis for the next-generation AI asset management platform: future competition in asset management will occur not merely between strategies, but between model capabilities, agent system capabilities, and research loop capabilities.

On the model side, GIM is developing proprietary temporal large models for financial applications. Compared to general-purpose models, these systems require stronger numerical reasoning, temporal awareness, and multi-market, multi-frequency data modeling capabilities to genuinely serve demanding tasks such as investment research and return forecasting.

On the agent side, GIM is reconstructing traditional research workflows through its self-evolving multi-agent system, CogAlpha. The core direction is not simply having models answer investment questions, but rather driving the research paradigm from heavy reliance on manual management toward human-machine collaborative semi-automated research, and ultimately toward an Auto Research closed loop capable of autonomously generating signals, evaluating signals, correcting hypotheses, and iterating continuously. As model capabilities advance and agent systems mature, AI's role in asset management is evolving from information processing and auxiliary analysis tools toward underlying infrastructure supporting research and decision-making. GIM's core paper on CogAlpha has been accepted to the main conference of ACL 2026, the premier NLP conference, with an Oral recommendation.

An Investment Bridging Two Eras

This financing carries symbolic weight as a bridge across eras. As one of the most representative institutions in modern quantitative investing, Renaissance Technologies used mathematical models and data science to profoundly shape the direction of global asset management over recent decades. Today, investors represented by founder Howard Morgan are once again betting on an AI-native asset management platform. If the first generation of asset management was defined by Warren Buffett-style subjective investors, and the second was reshaped by Renaissance-style quantitative firms, then the third generation of AI-native asset management platforms will compete not merely on strategy itself, but on model capabilities, agent system capabilities, and continuously evolving research loops. What GIM aims to advance is precisely the formation of this new generation of systems — not incremental efficiency gains to existing investment processes, but a systematic rewrite of how research, judgment, execution, and iteration are conducted.

While technology continues iterating, GIM's commercialization is also advancing. The company has established a Type 4/9 licensed entity in Hong Kong and is building out a private securities fund management vehicle in mainland China, currently in the AMAC filing process.

Meanwhile, the company has reached strategic partnerships with leading financial institutions for deep collaboration around AI-driven investment strategies; its first batch of AI-driven asset management products has completed AMAC filing and opened for fundraising. From proprietary models to product delivery, GIM has entered the live-fire stage of asset management.

For a company less than a year old that has closed three rounds in six months, the signal beyond the money itself is clearer: the people who best understand quantitative investing are starting to place serious bets on what's next.