Hustle Fund: AI Project Evaluation Focuses on Problem, Market, and Timing
Almost all startup pitches now have an AI angle, and Hustle Fund focuses on three core dimensions when filtering for real signals: problem, market, and timing.
Excellent AI products alone are not sufficient reasons for investment. If dozens of startups are chasing the same customer base, the opportunity often becomes crowded, making market timing a key variable.
Hustle Fund uses surfing as a metaphor: too early means the wave has not formed yet, too late means everyone is already on the wave; the goal is to catch the upcoming peak wave.
For example, Hustle Fund invested in Gamma before AI presentation and website building tools became a crowded space. At that time, the problem was clear, the market still had room, and the timing was right.
Now, similar tracks have changed significantly. New AI presentation tools must compete directly with companies that already have mature products, user bases, and growth momentum, facing a completely different market structure.
Horizontal AI tools target almost all users, covering writing, productivity, websites, and general software; vertical AI, on the other hand, delves into specific industries or scenarios, such as healthcare, law, insurance, and logistics, where there is still more whitespace.
For this reason, Hustle Fund currently has a higher interest in vertical AI. Horizontal AI is not "dead"; it has just become significantly harder to stand out in it today. When evaluating AI deals, one should not only ask if the technology is impressive but also question: Why solve this problem? Why enter this market? Why now? In the AI gold rush, timing is as important as the technology itself.
Who is buying: Early-stage capital (like Hustle Fund, which focuses on pre-seed/seed funding) prioritizes vertical AI where the timing window has not yet closed. Who is selling: Many founders are packaging general AI as a differentiated selling point. Event-driven: The proliferation of AI has led to increased homogenization of pitches. Funding is flowing into early vertical fields rather than saturated horizontal tools. Beneficiaries are teams that can occupy specific industry gaps; those under pressure are projects that enter crowded horizontal tracks late.
Source: Public information
ABAB AI Insight
Hustle Fund was founded by Elizabeth Yin and Eric Bahn in 2017, continuously supporting "hustler" founders with small early checks, emphasizing rapid experimentation over background prestige. The fund has grown from an initial size of about $11.5 million to subsequent funds of around $46 million, processing over 1,000 pitch applications monthly and long using AI to assist in the filtering process. The Gamma case shows that it entered the relevant field around 2020, when AI presentation and website tools had not yet formed intense competition.
In terms of funding pathways, Hustle Fund allocates capital to vertical or early horizontal projects where the timing window is still open, rather than chasing already mature tracks. Gamma subsequently completed about $68 million in Series B funding, achieving a valuation of $2.1 billion, led by Andreessen Horowitz, reaching a profitable $100 million ARR, with about 70 million users and a team of around 50, proving that early entry can capture subsequent capital and product momentum. The motivation is to avoid the homogenization pressure of horizontal tools and concentrate limited capital on deep applications in industries that still have whitespace.
Similar cases include early entrants in vertical SaaS companies (such as workflow tools for specific industries) and players entering the generative AI application layer around 2021-2022. The current industry is transitioning from late-stage expansion to control and differentiation: horizontal AI tool competition has intensified, while vertical AI is still in the penetration and embedding phase, with capital shifting from general tools to industry-specific solutions.
Essentially, this involves a transfer of pricing power and a restructuring of the industrial chain. After the rapid commoditization of AI foundational model capabilities, the technology itself no longer constitutes a barrier; true pricing power shifts to vertical applications that master industry data, workflow embedding, and timing windows. The horizontal track experiences increased user and capital concentration due to model layer homogenization, while vertical fields retain more early-entry pricing space due to regulatory, data, and scenario differences, leading capital to migrate to structures that are harder to replicate quickly.
ABAB News · Cognitive Laws
Technology is the entry ticket; timing is the pricing power.
In crowded tracks, early entrants sell time, while late entrants sell discounts.
Horizontal tools compete on speed, while vertical tools compete on depth.