What is non-dilutive funding?
Non-dilutive funding refers to capital that doesn't require giving up equity or ownership in your company. This includes government grants, research subsidies, tax credits, and milestone-based contracts.
In contrast, equity financing involves exchanging a portion of ownership for funding. Investors who provide equity usually receive shares and may gain influence over company decisions depending on their ownership percentage.
For AI startups, non-dilutive capital works well during early research, validation, or commercialization stages—especially when the technology is still developing or needs regulatory approval.
Key characteristics of non-dilutive funding include:
- No equity exchange: You get capital without giving up ownership stakes
- Full control: You keep complete decision-making authority
- Strategic flexibility: You maintain freedom to pivot without investor constraints
Why non-dilutive capital benefits AI startups
AI startups often face longer development timelines than traditional software companies. Building AI systems requires training large datasets, creating scalable models, and navigating complex regulatory frameworks. Many AI products also need ongoing iteration before they're ready for market.
Non-dilutive capital matches this pace by providing funding without pressure for quick returns. This money can support early research, data infrastructure, or pilot projects that won't generate immediate revenue.
Non-dilutive funding also provides an extended runway. AI companies often need more time to validate models, secure regulatory approval, or integrate with complex systems before they can generate revenue.
Types of non-dilutive funding options
1. Government grants and innovation programs
Government grants represent one of the most common sources of non-dilutive capital for AI startups. The Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs provide phased funding for early-stage R&D and commercialization.
Typical funding amounts:
- Phase I: Up to $275,000 for feasibility studies (6-12 months)
- Phase II: Up to $1.5 million for development (2 years)
Government agencies that actively support AI development include:
- National Science Foundation (NSF)
- Department of Defense (DOD)
- Department of Energy (DOE)
To qualify, companies typically need to be U.S.-based, for-profit businesses with fewer than 500 employees. The project must demonstrate technical innovation with commercial potential.
2. Research and development tax credits
R&D tax credits allow AI startups to recover a portion of their research expenses through tax incentives. Recent changes have made these credits more accessible to pre-revenue startups by allowing them to apply credits against payroll taxes instead of just income taxes.
To qualify, your startup must:
- Have less than $5 million in gross revenue for the tax year
- Have no gross receipts for more than five years
- Conduct qualifying R&D activities like developing algorithms or improving AI systems
Eligible startups can offset up to $500,000 in payroll taxes annually, providing immediate cash flow benefits even before profitability.
3. Revenue-based financing
Revenue-based financing provides upfront capital in exchange for a percentage of future revenue. Unlike traditional loans, repayments scale with your monthly income.
This option works best for AI startups that have already launched products and generate consistent revenue, even if modest. The funding typically ranges from $50,000 to $2 million depending on your monthly recurring revenue.
The repayment structure usually involves paying a fixed percentage (typically 3-8%) of monthly revenue until you've repaid a predetermined amount (often 1.3x-1.8x the original funding).
4. Corporate partnerships and innovation programs
Many large corporations offer programs that provide funding, technical resources, and market access to AI startups without taking equity.
Examples include:
- Google for Startups
- Microsoft for Startups Founder Hub
- AWS Activate
These programs typically provide:
- Cloud credits (often worth $100,000+)
- Technical support and resources
- Access to enterprise customers
- Mentorship from industry experts
While these programs don't provide direct cash like grants, the value of bundled services can significantly reduce startup costs and extend runway.
5. Non-equity accelerators and foundation grants
Some accelerators provide structured programs, mentorship, and funding without taking equity. Similarly, foundations offer grants to support mission-aligned work, especially in areas of public good.
Notable non-equity accelerators for AI startups include:
- MassChallenge
- Mozilla Builders
- AI2 Incubator (Allen Institute for AI)
Foundation grants typically range from $50,000 to $500,000 and often focus on AI applications with social impact, such as healthcare, education, or environmental solutions.
How to combine grants and equity financing
Using multiple funding types in a planned sequence is called "funding stacking." This approach lets AI startups fund different development stages with the most appropriate capital type.
Non-dilutive funding works well at the beginning of your startup journey—especially during research, prototyping, or validation. Because this funding doesn't require giving up equity, it allows you to complete early milestones before approaching investors.
Once you've achieved key technical milestones, you can pursue equity funding with a stronger position.
The non-dilutive funding helps by:
- Reducing technical risk
- Generating valuable data
- Creating proof points that justify a higher valuation
A basic funding timeline might look like this:
- Months 0-6: Apply for pre-seed grants
- Months 6-12: Use grant funds to build your minimum viable product
- Months 12-18: Apply for follow-on non-dilutive funding
- Months 18-24: Approach equity investors after reaching key milestones
Best practices for securing grants
1. Focus on clear technical objectives
Grant reviewers evaluate proposals based on how clearly you define what you aim to achieve. For AI startups, this means breaking down your development process into measurable, time-bound milestones.
For example, instead of saying "we will improve our model," specify: "Train a transformer-based NLP model to reach 90% accuracy on a dataset of 50,000 medical transcripts within six months."
What to emphasize:
- Quantifiable goals (accuracy percentages, dataset sizes)
- Defined timelines for each milestone
- Relevance of each objective to the grant's focus
2. Align with agency priorities
Federal agencies fund projects that match their mission areas. Successful applicants research the agency's strategic goals and connect their project to those priorities.
For example, if the Department of Energy funds AI tools that improve energy efficiency, an AI startup building a reinforcement learning system for optimizing building energy use should highlight those energy-saving benefits.
You can find agency priorities in:
- Agency solicitations (SBIR topics)
- Strategic frameworks
- Congressional budget justifications
3. Address ethical AI considerations
AI proposals should address how the technology will be implemented responsibly. Reviewers look for evidence that you understand relevant risks and have mitigation strategies.
Common AI-specific compliance elements include:
- Data privacy: How you'll protect sensitive information in training data
- Algorithmic fairness: How you'll prevent or mitigate bias
- Transparency: How your AI's decisions can be explained or audited
Including these elements shows that your team understands the policy environment shaping AI development.
4. Apply to multiple programs
A single grant rarely supports all phases of AI development. A multi-grant strategy involves applying to several programs that fund different stages of the same project.
For example, you might apply for a Phase I SBIR from NSF for feasibility testing, while also submitting to a DOD program for a specific application of your technology.
This approach requires coordination but provides more flexible funding for long-term R&D projects.
Using AI tools to streamline applications
AI-powered tools can help with the grant writing process by handling time-consuming tasks. These tools can identify funding opportunities, draft technical content, and check for formatting issues.
AI startups can also use their own technical expertise to strengthen applications. For example, natural language processing teams can analyze past winning proposals, while data science teams can organize research data to demonstrate feasibility.
Efficiency gains from AI tools include:
- Automated research: Finding relevant funding opportunities based on keywords and eligibility
- Content generation: Creating initial drafts of technical descriptions and team bios
- Compliance checking: Reviewing proposals for formatting, page limits, and required attachments
Planning your non-dilutive funding strategy
Start by identifying opportunities that match your development stage. Early-stage AI startups should focus on research-oriented grants like SBIR Phase I. If your product is closer to validation, look for Phase II options or corporate innovation programs.
Create a simple 6-12 month roadmap:
- First 1-2 months: Gather technical documentation and assess eligibility
- Months 2-4: Submit applications to targeted programs
- Months 5-8: Follow up on applications and prepare for interviews
- Months 9-12: Plan for follow-on funding and milestone reporting
Track your success using metrics like:
- Number of submitted applications
- Award rate
- Total non-dilutive capital secured
- Milestones achieved using grant funds
Ready to explore your non-dilutive funding options?
Start your free funding assessment to discover which grants your AI startup qualifies for.
Frequently asked questions about non-dilutive funding
How can I calculate the return on investment for grant applications?
Calculate ROI by dividing the potential grant amount by the total cost of preparing the application (time and expenses). Also consider indirect benefits like increased credibility, validated technology, and preparation for future funding rounds.
Which AI technologies receive the most government funding support?
Government agencies typically prioritize AI technologies with dual-use applications (commercial and public sector), transparent systems with bias mitigation features, and projects addressing national priorities like healthcare, cybersecurity, and climate technology.
What documents should AI startups prepare before applying for grants?
Prepare a technical development roadmap, preliminary performance data, team bios highlighting relevant expertise, and documentation of any existing partnerships or prior research that demonstrates your capability to execute the proposed work.
