Independent Research Initiative
An empirical research project producing verified data on the time, cost, and feasibility for individual developers to build functional software alternatives to commercial SaaS products using AI-assisted coding tools.
A bounded, empirical question — defined precisely to produce useful data.
There is a persistent and largely unresolved debate: does AI-assisted development fundamentally alter the economics of building software? One side contends that modern SaaS products are newly replicable by individual developers working with AI coding tools. The other argues that commercial software represents far more than its feature set.
Rather than adjudicating this debate through argument, this project tests it empirically. We measure one specific variable: the development cost barrier — the time, money, and effort required for an individual developer to produce software that meets a defined functional specification, using AI-assisted coding tools.
Specifications are derived from the public-facing documentation of commercial SaaS products. Every submission is independently reviewed for specification compliance, production readiness, and code quality. Over time, tracking these metrics reveals whether AI tools are meaningfully reducing this barrier — and by how much.
Metrics Under Active Collection
Hours to Completion
Developer time logged per submission
AI Inference Cost
Token consumption and estimated compute cost
Production Readiness Score
Code quality, security posture, spec compliance
Specification Category
SaaS product vertical and complexity tier
Precisely defining what we do not measure is as important as defining what we do.
The development cost barrier is one of many factors that determine whether a software incumbent faces genuine competitive pressure. Reducing that barrier does not, in isolation, constitute an existential threat to any product or company. This project makes no claims beyond its defined measurement.
Commercial software incumbents maintain advantages beyond their feature sets: distribution networks, customer trust, compliance certifications, integrations, support organizations, and years of accumulated product refinement. None of these factors are captured in a functional specification derived from public documentation.
This research measures one input variable. The broader question of competitive dynamics in software markets requires a far more complex model — and is not the subject of this project.
Primary data collected through a structured, incentivized developer program.
01
Developers select a software specification and use AI-assisted coding tools to build a functional implementation. Upon submission, they earn bounty points through SaasBounties.com. No code is copied; no reverse engineering is permitted.
02
Each submission includes the complete git repository and the developer's AI prompt history. The prompt history enables computation of hours spent, tokens consumed, and estimated AI inference cost. All submissions are reviewed privately and confidentially.
03
Submissions undergo automated and manual review for specification compliance, production readiness, security vulnerability assessment, and code quality. Only submissions meeting the defined specification earn bounty points and contribute to the dataset.
Specifications are constructed from the public-facing documentation of commercial SaaS products and represent a defined minimum functional threshold. They are not derived from proprietary information, source code, or internal systems. Specifications are published openly; the resulting code submissions are not.
What the accumulated data will reveal — and how it can be accessed.
As the dataset reaches meaningful scale, findings will be published as research reports examining trends in development cost, AI tool effectiveness by product category, production readiness trajectories, and the relationship between specification complexity and effort required.
Register below to receive research publications and dataset availability notices.
The underlying dataset — including verified build times, token costs, production readiness scores, and specification compliance rates across product categories — is available to qualified institutional buyers.
The dataset is of potential value to investment analysts examining software market dynamics, media organizations covering the AI and technology industry, academic researchers, and corporate strategy functions at technology companies.
Contact us to discuss data access, licensing, and research collaboration.
For editorial inquiries, research commentary, data requests, or background briefings on the project methodology and preliminary findings, please contact us at press@aialternatives.org.
For dataset licensing, research collaboration, or institutional access to findings, contact research@aialternatives.org. We work with analysts, academics, and strategy teams on a confidential basis.
Developers interested in contributing to the project by building and submitting software implementations should visit SaasBounties.com for active bounty listings, specification details, and submission guidelines.
For all other inquiries: hello@aialternatives.org