A Dream Come True For Grantx: DeepSeek and the Shift to Open-Source

The AI landscape is undergoing a seismic shift. DeepSeek, an innovative new foundational model, has disrupted conventional wisdom by developing a world-class AI model at a fraction of the cost typically required for such an achievement. This development has profound implications—not just for the tech giants scrambling to reassess their AI strategies, but also for companies like Grantx, which operate at the application layer and now have access to cheaper, more efficient AI models tailored to their unique needs.

DeepSeek’s breakthrough signals a broader transition: the commoditization of foundational AI models and the growing importance of domain-specific applications. For Grantx, this represents an opportunity to accelerate its timeline for training proprietary AI models on grant-writing best practices, enhance transparency in funding recommendations, and extend its capabilities as a vertical agent ahead of schedule.

Vertical AI: The Real Battleground for Innovation

With the cost of training large models plummeting, the next major AI battle will not be about foundational models—but rather who can apply AI most effectively to domain-specific problems.

Why This Matters for Grantx

This shift to open-source and cheaper alternatives to OpenAI or Google's Gemini also creates the opportunity for Grantx to begin more advanced implementations of custom-models earlier than previously road mapped, at a significantly lower cost than originally anticipated. Now, we're reevaluating the timeline for adding reinforcement learning to the platform, along with multi-step agentic assistance, to significantly improve both the quality of grant match-making and writing, but also expand the various time-saving capabilities of the platform.

It is also important to note that this disruption of the foundational model landscape futher strengthens Grantx's differentiation.

Grantx operates in a highly specialized domain: helping organizations find, evaluate, and apply for grants. Unlike general-purpose AI chatbots, Grantx’s AI needs to understand:

• The hierarchies of grantmaking (e.g., how NIH SBIR requirements differ from DOE clean energy RFPs)

• The funding priorities of different agencies (e.g., how a research grant for climate tech might have vastly different requirements from one focused on community development)

• The compliance requirements and equity mandates that govern many funding programs

This is where domain-specific AI has the advantage. Open-source AI models provide a strong foundation, but the real competitive edge lies in proprietary datasets and specialized workflows.

By training AI models on its large and proprietary grant database, Grantx can develop custom embeddings that make its AI far more effective than generic models. This level of specificity is a fantastic moat—foundational models may be available to everyone, but the expertise layered on top is what will differentiate Grantx from competitors.

Transparency, Bias Auditing, and Compliance: The Open-Source Advantage

Another major benefit of the shift to open-source AI is transparency. One of the biggest criticisms of closed-source AI models like OpenAI’s GPT-4 is the black box problem—users can’t see why a model made a particular recommendation.

For Grantx, this is a critical issue, especially when working with government agencies and nonprofits that need to ensure data security and equitable grant distribution.

How Open-Source Helps Solve This Problem

• Bias Auditing: Because Grantx can fine-tune and inspect open-source models, it can directly assess and mitigate bias in grant recommendations.

• Regulatory Compliance: Many government-funded programs require explainability in decision-making. Open-source AI makes it possible to audit and document how funding recommendations are made—something competitors using closed AI APIs will struggle to replicate.

• Accessibility for Underserved Institutions: Open-source models can be optimized for lower-cost deployment, allowing Grantx to provide AI-powered grant writing assistance even to NGOs running on outdated infrastructure.

How Can Grantx Capitalize?

DeepSeek’s success signals a broader shift toward AI cost reduction and application-layer innovation. For Grantx, the roadmap is clear:

1. Deploy RAG Pipelines at a Fraction of the Cost

   • With inference costs dropping 3–5x, Grantx can scale AI-powered grant matching and writing tools more aggressively than previously planned.

2. Develop Proprietary AI for Grant Writing

   • Training custom models on grant-specific language will allow Grantx to outperform generic AI in suggesting funding strategies, improving proposal structure, and aligning applications with funder priorities.

3. Expand into High-Touch, Regulated Sectors

   • Lower AI costs allow Grantx to target federal grants, university research programs, and compliance-heavy infrastructure organizations that require a high degree of AI explainability.

The Bottom Line: AI is No Longer About Who Has the Most Compute—It’s About Who Uses It Best

DeepSeek’s rise shows that the future of AI isn’t about who has the biggest data center—it’s about who applies AI most effectively. The commoditization of base models frees up companies like Grantx to focus on specialization, transparency, and user impact.

For Grantx, this means faster innovation, lower costs, and a deeper moat in the grant funding space. The future of AI isn’t just open—it’s intelligent, industry-specific, and ready for real-world applications. And Grantx is leading the charge.

Written by Lindell Cumes (COO of Grantx) & Jacob Schoenberg (Founder/CEO of Grantx)