# Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method

arXiv: https://arxiv.org/abs/2105.03360 · HTML: https://mlsanity.com/paper/2105.03360/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 94 references that are in the map.
- First posted on arXiv 7 May 2021.

## Built on by (most cited first, 10 of 10)

- [A Fused Large Language Model for Predicting Startup Success](https://mlsanity.com/paper/2409.03668/) (2024)
- [Improving Startup Success with Text Analysis](https://mlsanity.com/paper/2312.06236/) (2023)
- [Using Deep Learning to Find the Next Unicorn: A Practical Synthesis](https://mlsanity.com/paper/2210.14195/) (2022)
- [VCBench: Benchmarking LLMs in Venture Capital](https://mlsanity.com/paper/2509.14448/) (2025)
- [DIALECTIC: A Multi-Agent System for Startup Evaluation](https://mlsanity.com/paper/2603.12274/) (2026)
- [Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data](https://mlsanity.com/paper/2505.24622/) (2025)
- [Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agents](https://mlsanity.com/paper/2512.22608/) (2025)
- [Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach](https://mlsanity.com/paper/2601.16568/) (2026)
- [Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems](https://mlsanity.com/paper/2309.16888/) (2023)
- [AI for Investment: A Platform Disruption](https://mlsanity.com/paper/2311.06251/) (2023)

## Abstract (arXiv)

Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting soft information (information that cannot be quantified) and making predictions in unknowable risk situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the gold standard for assessing soft signals and make use of intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.