Online communities offer their members various benefits, such as information access, social and emotional support, and entertainment. Despite the important role that founders play in shaping communities, prior research has focused primarily on what drives users to participate and contribute; the motivations and goals of founders remain underexplored. To uncover how and why online communities get started, we present findings from a survey of 951 recent founders of Reddit communities. We find that topical interest is the most common motivation for community creation, followed by motivations to exchange information, connect with others, and self-promote. Founders have heterogeneous goals for their nascent communities, but they tend to privilege community quality and engagement over sheer growth. These differences in founders' early attitudes towards their communities help predict not only the community-building actions that they pursue, but also the ability of their communities to attract visitors, contributors, and subscribers over the first 28 days. We end with a discussion of the implications for researchers, designers, and founders of online communities.
Predicting startup success from founder career data is hard. The signal is weak, the labels are rare (9%), and most founders who succeed look almost identical to those who fail. We engineer 28 structured features directly from raw JSON fields -- jobs, education, exits -- and combine them with a deterministic rule layer and XGBoost boosted stumps. Our model achieves Val F0.5 = 0.3030, Precision = 0.3333, Recall = 0.2222 -- a +17.7pp improvement over the zero-shot LLM baseline. We then run a controlled experiment: extract 9 features from the prose field using Claude Haiku, at 67% and 100% dataset coverage. LLM features capture 26.4% of model importance but add zero CV signal (delta = -0.05pp). The reason is structural: anonymised_prose is generated from the same JSON fields we parse directly -- it is a lossy re-encoding, not a richer source. The ceiling (CV ~= 0.25, Val ~= 0.30) reflects the information content of this dataset, not a modeling limitation. In characterizing where the signal runs out and why, this work functions as a benchmark diagnostic -- one that points directly to what a richer dataset would need to include.
Online communities are an increasingly important stakeholder for firms, and despite the growing body of research on them, much remains to be learned about them and about the factors that determine their attributes and sustainability. Whereas most of the literature focuses on predictors such as community activity, network structure, and platform interface, there is little research about behavioral and psychological aspects of community members and leaders. In the present study we focus on the personality traits of community founders as predictors of community attributes and sustainability. We develop a tool to estimate community members' Big Five personality traits from their social media text and use it to estimate the traits of 35,164 founders in 8,625 Reddit communities. We find support for most of our predictions about the relationships between founder traits and community sustainability and attributes, including the level of engagement within the community, aspects of its social network structure, and whether the founders themselves remain active in it.
We present a comparative docking experiment that aligns human-subject interview data with large language model (LLM)-driven synthetic personas to evaluate fidelity, divergence, and blind spots in AI-enabled simulation. Fifteen early-stage startup founders were interviewed about their hopes and concerns regarding AI-powered validation, and the same protocol was replicated with AI-generated founder and investor personas. A structured thematic synthesis revealed four categories of outcomes: (1) Convergent themes - commitment-based demand signals, black-box trust barriers, and efficiency gains were consistently emphasized across both datasets; (2) Partial overlaps - founders worried about outliers being averaged away and the stress of real customer validation, while synthetic personas highlighted irrational blind spots and framed AI as a psychological buffer; (3) Human-only themes - relational and advocacy value from early customer engagement and skepticism toward moonshot markets; and (4) Synthetic-only themes - amplified false positives and trauma blind spots, where AI may overstate adoption potential by missing negative historical experiences. We interpret this comparative framework
Family-firm scholarship offers competing predictions about whether family control protects or threatens market integrity. We argue that the answer depends on how family involvement is exercised. Drawing on socioemotional wealth and agency-entrenchment perspectives, we examine 8,634 U.S. firm-years (2007-2018) and link family-firm constructs to exchange-generated surveillance flags from NASDAQ SMARTS. Founder-CEO control is associated with approximately 9.5% fewer flags, family governance involvement with 21.3% more, and deep multi-generational family control with 47.1% more. The findings reveal heterogeneous identity and entrenchment mechanisms within family firms and connect family-firm governance to a market-integrity outcome previously absent from the literature.
We introduce a compact pangenome representation based on an optimal segmentation concept that aims to reconstruct founder sequences from a multiple sequence alignment (MSA). Such founder sequences have the feature that each row of the MSA is a recombination of the founders. Several linear time dynamic programming algorithms have been previously devised to optimize segmentations that induce founder blocks that then can be concatenated into a set of founder sequences. All possible concatenation orders can be expressed as a founder block graph. We observe a key property of such graphs: if the node labels (founder segments) do not repeat in the paths of the graph, such graphs can be indexed for efficient string matching. We call such graphs segment repeat-free founder block graphs. We give a linear time algorithm to construct a segment repeat-free founder block graph given an MSA. The algorithm combines techniques from the founder segmentation algorithms (Cazaux et al. SPIRE 2019) and fully-functional bidirectional Burrows-Wheeler index (Belazzougui and Cunial, CPM 2019). We derive a succinct index structure to support queries of arbitrary length in the paths of the graph. Experiments
Foundation Models (FMs) and World Models (WMs) offer complementary strengths in task generalization at different levels. In this work, we propose FOUNDER, a framework that integrates the generalizable knowledge embedded in FMs with the dynamic modeling capabilities of WMs to enable open-ended task solving in embodied environments in a reward-free manner. We learn a mapping function that grounds FM representations in the WM state space, effectively inferring the agent's physical states in the world simulator from external observations. This mapping enables the learning of a goal-conditioned policy through imagination during behavior learning, with the mapped task serving as the goal state. Our method leverages the predicted temporal distance to the goal state as an informative reward signal. FOUNDER demonstrates superior performance on various multi-task offline visual control benchmarks, excelling in capturing the deep-level semantics of tasks specified by text or videos, particularly in scenarios involving complex observations or domain gaps where prior methods struggle. The consistency of our learned reward function with the ground-truth reward is also empirically validated. Our
Large language model (LLM) families are improving rapidly, yet it remains unclear how quickly multimodal capabilities emerge and propagate within open families. Using the ModelBiome AI Ecosystem dataset of Hugging Face model metadata and recorded lineage fields (>1.8x10^6 model entries), we quantify multimodality over time and along recorded parent-to-child relations. Cross-modal tasks are widespread in the broader ecosystem well before they become common within major open LLM families: within these families, multimodality remains rare through 2023 and most of 2024, then increases sharply in 2024-2025 and is dominated by image-text vision-language tasks. Across major families, the first vision-language model (VLM) variants typically appear months after the first text-generation releases, with lags ranging from ~1 month (Gemma) to more than a year for several families and ~26 months for GLM. Lineage-conditioned transition rates show weak cross-type transfer: among fine-tuning edges from text-generation parents, only 0.218% yield VLM descendants. Instead, multimodality expands primarily within existing VLM lineages: 94.5% of VLM-child fine-tuning edges originate from VLM parents,
While founder backgrounds account for less than 4% of funding variation among Y Combinator startups, this suggests that other factors, such as industry trends and product innovation, may play a more significant role in funding outcomes. Using data on 4,323 YC companies from 2005-2024 merged with S&P Global funding data, I estimate OLS regressions with batch year fixed effects on a regression sample of 2,113 companies. The coefficient on prior FAANG work experience is -0.251, indicating approximately 22% less funding. However, this result is not robust, as it changes direction in further analyses, suggesting that FAANG experience may not be a reliable predictor of funding. The most robust finding is that startups within Y Combinator that consist of larger founding teams tend to raise more funding, with each additional co-founder associated with approximately 21% more capital raised. While observable credentials such as prior FAANG work experience and top-tier education explain minimal variation in funding, the size of the founding team emerges as a more consistent predictor, highlighting the importance of team dynamics in securing capital. Unobserved factors like industry and pr
Open digital public infrastructure needs community management to ensure accountability, sustainability, and robustness. Yet open-source projects often rely on centralized decision-making, and the determinants of successful community management remain unclear. We analyze 637 GitHub repositories to trace transitions from founder-led to shared governance. Specifically, we document trajectories to community governance by extracting institutional roles, actions, and deontic cues from version-controlled project constitutions GOVERNANCE .md. With a semantic parsing pipeline, we cluster elements into broader role and action types. We find roles and actions grow, and regulation becomes more balanced, reflecting increases in governance scope and differentiation over time. Rather than shifting tone, communities grow by layering and refining responsibilities. As transitions to community management mature, projects increasingly regulate ecosystem-level relationships and add definition to project oversight roles. Overall, this work offers a scalable pipeline for tracking the growth and development of community governance regimes from open-source software's familiar default of founder-ownership.
This paper investigates how individual entrepreneurs can turn creative ideas into successful solo businesses in an era increasingly shaped by Artificial Intelligence (AI) agents. It highlights the key steps that connect personal vision, structured experimentation, and lasting value creation, and shows how AI agents can act as digital co-founders throughout this journey. Building on research in entrepreneurship, creativity, and innovation, we present a framework with three key stages: (1) Imagination shaping, where vague goals become clear value propositions, supported by AI agents that help with market scanning, idea refinement, and rapid concept generation; (2) Reality testing, where these ideas are tested through low-cost experiments, structured feedback loops, and efficient execution, with AI agents automating tasks such as prototyping, content creation, customer interaction, and data analysis; and (3) Reality scaling, where successful ideas are transformed into repeatable processes, scalable market strategies, and long-term business models, increasingly operated and optimized by autonomous or semi-autonomous AI workflows. We focus on the specific context of solopreneurship, cha
In a study of 107 male Y Combinator founders, a surprising correlation between age-normalized testosterone and company stage was uncovered. Testosterone, a hormone associated with confidence, dominance, and drive, increased by 55.7% from pre-seed to seed funding, peaking at the Series B stage, where levels were 99.6% higher than pre-seed. After series B funding, testosterone was observed to drop by 42.2%, coinciding with a spike in cortisol levels. This age-normalized biomarker analysis supports the dual-hormone hypothesis, illustrating that early startup success fosters feelings of dominance and confidence, while later-stage pressures and stresses erode these feelings. An alternative interpretation of the data, which suggests the opportunity for a longitudinal study, is that male founders with higher testosterone are more likely to raise larger rounds of funding.
We examine the distributional properties of a Feller diffusion $(X(τ))_{τ\in [0, t]}$ conditioned on the current population $X(t)$ having a single ancestor at time zero. The approach is novel and is based on an interpretation of Feller's original solution according to which the current population is comprised of a Poisson number of exponentially distributed families, each descended from a single ancestor. The distribution of the number of ancestors at intermediate times and the joint density of coalescent times is determined under assumptions of initiation of the process from a single ancestor at a specified time in the past, including infinitely far in the past, and for the case of a uniform prior on the time since initiation. Also calculated are the joint distribution of the time since the most recent common ancestor of the current population and the contemporaneous population size at that time under different assumptions on the time since initiation. In each case exact solutions are given for supercritical, critical and subcritical diffusions. For supercritical diffusions asymptotic forms of distributions are also given in the limit of unbounded exponential growth.
This study explores the application of large language models (LLMs) in venture capital (VC) decision-making, focusing on predicting startup success based on founder characteristics. We utilize LLM prompting techniques, like chain-of-thought, to generate features from limited data, then extract insights through statistics and machine learning. Our results reveal potential relationships between certain founder characteristics and success, as well as demonstrate the effectiveness of these characteristics in prediction. This framework for integrating ML techniques and LLMs has vast potential for improving startup success prediction, with important implications for VC firms seeking to optimize their investment strategies.
Startup companies solve many of today's most complex and challenging scientific, technical and social problems, such as the decarbonisation of the economy, air pollution, and the development of novel life-saving vaccines. Startups are a vital source of social, scientific and economic innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm's founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors as well as the team's size. The effects of founders' personalities on the success of new ventures are mainly unknown. Here we show that founder personality traits are a significant feature of a firm's ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups. We found that the Big 5 personality traits of startup founders across 30 dimensions significantly differed from that of the population at large. Key personality fac
This research introduces an innovative evaluation method for the "founder-idea" fit in early-stage startups, utilizing advanced large language model techniques to assess founders' profiles against their startup ideas to enhance decision-making. Embeddings, self-play, tree-of-thought, and critique-based refinement techniques show early promising results that each idea's success patterns are unique and they should be evaluated based on the context of the founder's background.
Indexing labeled graphs for pattern matching is a central challenge of pangenomics. Equi et al. (Algorithmica, 2022) developed the Elastic Founder Graph ($\mathsf{EFG}$) representing an alignment of $m$ sequences of length $n$, drawn from alphabet $Σ$ plus the special gap character: the paths spell the original sequences or their recombination. By enforcing the semi-repeat-free property, the $\mathsf{EFG}$ admits a polynomial-space index for linear-time pattern matching, breaking through the conditional lower bounds on indexing labeled graphs (Equi et al., SOFSEM 2021). In this work we improve the space of the $\mathsf{EFG}$ index answering pattern matching queries in linear time, from linear in the length of all strings spelled by three consecutive node labels, to linear in the size of the edge labels. Then, we develop linear-time construction algorithms optimizing for different metrics: we improve the existing linearithmic construction algorithms to $O(mn)$, by solving the novel exclusive ancestor set problem on trees; we propose, for the simplified gapless setting, an $O(mn)$-time solution minimizing the maximum block height, that we generalize by substituting block height with
We introduce an information-theoretic generalization of the fixation statistic, the Tsallis-order $q$ F-statistic, $F_q$, which measures the fraction of Tsallis $q$-entropy lost within subpopulations relative to the pooled population. The family nests the classical variance-based fixation index $F_{\textbf{ST}}$ at $q{=}2$ and a Shannon-entropy analogue at $q{=}1$, whose absolute form equals the mutual information between alleles and population labels. By varying $q$, $F_q$ acts as a spectral differentiator that up-weights rare variants at low $q$, while $q{>}1$ increasingly emphasizes common variants, providing a more fine-grained view of differentiation than $F_{\textbf{ST}}$ when allele-frequency spectra are skewed. On real data (865 Oceanian genomes with 1,823,000 sites) and controlled genealogical simulations (seeded from 1,432 founders from HGDP and 1000 Genomes panels, with 322,216 sites), we show that $F_q$ in One-vs-Rest (OVR) and Leave-One-Out (LOO) modes provides clear attribution of which subpopulations drive regional structure, and sensitively timestamps isolation-migration events and founder effects. $F_q$ serves as finer-resolution complement for simulation audits
Benchmarks such as SWE-bench and ARC-AGI demonstrate how shared datasets accelerate progress toward artificial general intelligence (AGI). We introduce VCBench, the first benchmark for predicting founder success in venture capital (VC), a domain where signals are sparse, outcomes are uncertain, and even top investors perform modestly. At inception, the market index achieves a precision of 1.9%. Y Combinator outperforms the index by a factor of 1.7x, while tier-1 firms are 2.9x better. VCBench provides 9,000 anonymized founder profiles, standardized to preserve predictive features while resisting identity leakage, with adversarial tests showing more than 90% reduction in re-identification risk. We evaluate nine state-of-the-art large language models (LLMs). DeepSeek-V3 delivers over six times the baseline precision, GPT-4o achieves the highest F0.5, and most models surpass human benchmarks. Designed as a public and evolving resource available at vcbench.com, VCBench establishes a community-driven standard for reproducible and privacy-preserving evaluation of AGI in early-stage venture forecasting.
LLM based agents have recently demonstrated strong potential in automating complex tasks, yet accurately predicting startup success remains an open challenge with few benchmarks and tailored frameworks. To address these limitations, we propose the Startup Success Forecasting Framework, an autonomous system that emulates the reasoning of venture capital analysts through a multi agent collaboration model. Our framework integrates traditional machine learning methods such as random forests and neural networks within a retrieval augmented generation framework composed of three interconnected modules: a prediction block, an analysis block, and an external knowledge block. We evaluate our framework and identify three main findings. First, by leveraging founder segmentation, startups led by L5 founders are 3.79 times more likely to succeed than those led by L1 founders. Second, baseline large language models consistently overpredict startup success and struggle under realistic class imbalances largely due to overreliance on founder claims. Third, our framework significantly enhances prediction accuracy, yielding a 108.3 percent relative improvement over GPT 4o mini and a 30.8 percent rela