High-dimensional decision-making tasks, such as business partner selection, involve evaluating large candidate pools with heterogeneous numerical, categorical, and textual features. MAS, a hierarchical multi-agent framework that decomposes evaluation into three layers: a Planner Agent that designs strategies, Specialized Agents that perform role-specific assessments, and a Supervisor Agent that integrates their outputs. To support systematic evaluation, we also introduce a curated benchmark dataset of venture capital co-investments, featuring diverse firm attributes and ground-truth syndicates. MAS consistently outperforms single-agent and debate-based multi-agent baselines, achieving up to 10-15% higher match rates. Analysis of agent reasoning shows that planners are most responsive to domain-informed prompts, specialists produce complementary feature coverage, and supervisors play an important role in aggregation. Our implementation is available at this anonymous link. In real-world decision-making, practitioners often navigate high-dimensional data including extensive option sets and numerous evaluative features (Sandanayake et al., 2018; Sigle et al., 2023). Business partner selection which includes partner shortlisting and strategic alliance formation exemplifies this challenge (Mindruta et al., 2016): firms often face a vast pool of potential candidates, each described by diverse attributes ranging from quantitative indicators (e.g., financial metrics, geographic presence) to text-rich information (e.g., strategic fit, investment preferences) (Shah & Swaminathan, 2008). The scale and complexity of such data can easily overwhelm human decision-makers, incurring significant costs (Li et al., 2008). This underscores the need for intelligent systems capable of analyzing large candidate sets and diverse features. Large language models (LLMs) have emerged as promising tools for addressing reasoning tasks in data-rich domains (Lee et al., 2025; Mischler et al., 2024). With appropriate prompting (e.g., few-shot learning) or information retrieval techniques (e.g., RAG), these models can identify salient features using only feature and task descriptions, achieving performance comparable to established methods (Li et al., 2025a; Jeong et al., 2024).

ChatGPT and its fellow chatbots may be much talked about (and talked to: ChatGPT may now have more than 100m users). But Mr Tossell's newsletter hints that the real action in generative AI is increasingly in all manner of less chatty services enabled by foundation models. Each model is trained on reams of text, images, sound files or any other heap of data. This allows them to interpret, react to and create statements in natural language, as well as art, music and any other type of content you find on the internet. Even as the venture-capital (VC) industry nurses a giant hangover after the recent tech crash put paid to a bubbly couple of years, entrepreneurs experimenting with generative AI have no trouble attracting investments.

Since ChatGPT was launched in November, a new mini-industry has mushroomed that has defied the broader slump in tech. Not a week goes by without someone unveiling a "generative" artificial intelligence (AI) underpinned by "foundation" models--the large and complex algorithms that give ChatGPT and other AIs like it their intelligence. On February 24th Meta, Facebook's parent company, released a model called LLaMA. This week it was reported that Elon Musk, the billionaire boss of Tesla and Twitter, wants to create an AI that would be less "woke" than ChatGPT. One catalogue, maintained by Ben Tossell, a British tech entrepreneur, and shared in a newsletter, has recently grown to include, among others, Ask Seneca (which answers questions based on the writings of the stoic philosopher), Pickaxe (which analyses your own documents), and Issac Editor (which helps students write academic papers).

With close to driverless cars, robots to automate menial tasks, smart virtual assistants to solve your doubts in milliseconds or robots to map diseases, AI is the heart of most intelligent products and services today. The global AI market was valued at $62.35 billion in 2020 and is only expected to develop at a compound annual growth rate of 40 per cent in the next decade. Given this colossal maturation, it comes with no surprise that VC firms are investing in the technology at a heavy pace. VC investors across public and private sectors are becoming increasingly interested in having their share in this tremendously growing sector. PWC's 2020 Money Tree Report outlined AI as an emerging area in the market, with $11.5 billion being invested in AI companies during the first three quarters of last year.

The future of technology is determined by a handful of venture capitalists. The world's 10 leading venture capital firms have, together, invested over $150 billion in technology startups. The venture capitalists who run these firms decide which startups today will develop the new platforms and technologies that will shape our lives tomorrow. There is a startling lack of diversity within the venture capital sector. This means that a small group of men -- mostly white men -- make decisions that affect all of us. Unsurprisingly, they all too often ignore the broader societal and human rights implications of these investment decisions.

Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that rewards can possibly be exponential. Though various empirical studies and data-driven modeling work have been done, the predictive power of the complex networks of stakeholders including venture capital investors, start-ups, and start-ups' managing members has not been thoroughly explored. We design an incremental representation learning mechanism and a sequential learning model, utilizing the network structure together with the rich attributes of the nodes. In general, our method achieves the state-of-the-art prediction performance on a comprehensive dataset of global venture capital investments and surpasses human investors by large margins. Specifically, it excels at predicting the outcomes for start-ups in industries such as healthcare and IT. Meanwhile, we shed light on impacts on start-up success from observable factors including gender, education, and networking, which can be of value for practitioners as well as policy makers when they screen ventures of high growth potentials.

These are nascent efforts but one forecast suggests adoption is about to pick up. AI will be involved in 75% of venture capital investment decisions by 2025, up from less than 5% today, according to a recent Gartner Inc. forecast. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. AI's ability to recognize patterns in data and predict likely outcomes has raised hopes that it can play a bigger role in decision-making in fields such as finance and healthcare. Now, similar bets are being placed in venture capital.

Roche, a Swiss healthcare giant with global reach, teams up with one of the leading Israeli healthcare and life sciences VC firm, aMoon, on a collaborative investment program aimed at accelerating innovative diagnostic technologies from Israel's healthtech ecosystem. The collaboration, called "StarFinder Lab," will provide funding, mentoring, and strategic support to "9 Stars," newly-formed or existing ventures, elected through the program. The partnership focuses on identifying and cultivating disruptive AI-driven data as well as digital healthcare solutions from early-stage startups providing different technological solutions for the many different moving aspects of healthcare. "Healthtech and AI are transforming the healthcare industry as we know it, and we see great value in working with Israeli healthcare innovators as they build their companies," said Michele Pedrocchi, Head of Global Strategy and Business Development at Roche Diagnostics. The joint venture will create an innovative atmosphere, where selected startups will receive access to global expertise from the Roche and aMoon.

The venture capital (VC) world often follows the general trends of the markets. When social media is the in-thing, investors will flock to all manner of social media startups. The same goes for any area of investing from mobile apps to live-work-play co-working places and everything in between. So too is the investor perspective on artificial intelligence. When it became clear less than a decade ago that AI was the latest, hottest place to build companies that could grow from tiny startups to huge public market exits of acquisitions, the VC community got all in.

With an unavoidable recession in the cards, what can we expect in the coming months? How quickly can markets, societies and small businesses recover? The past decade was successful overall, showcasing phenomenal four-times S&P growth and close to seven-times Nasdaq growth. After crashing in March 2009, both rose in the face of adversity by mid-February 2020. But that all changed by March 2020.