Utilizing AI in Grant Writing and Project Financing for Renewable Energy

At OpenAI's DevDay, I had the pleasure of meeting Helena Merk, the CEO and founder behind Streamline Climate. Streamline is rethinking the way climate tech startups secure vital government funding, but leveraging AI to navigate, automate, and optimize the labyrinthine grant application process.

In our interview from March (before the release of GPT-4o or Claude 3.5 Sonnet), Helena shares insights on how Streamline Climate is not just saving time, but potentially accelerating our global response to climate change. From tackling bureaucratic hurdles to leveling the playing field for innovators, discover how AI is reshaping the landscape of climate tech funding and possibly our planet's future.

Four key takeaways

Bureaucracy is a silent killer. In Helena's experience, many climate grants are ineligible right out of the gate because of incorrect clerical errors or misunderstandings of the grant's eligibility criteria. Ultimately, this wasted time and effort compounds across an entire industry.

Half of the reason you would get rejected is because of a clerical error on submitting your files correctly, or filing for something for which you're not eligible, the grant was never meant for you. So people are spending, on average, over 100 hours on a single grant application. And half of those have zero chance of ever winning.

Prompt engineering wins out over fine-tuning. OpenAI's advice to Streamline (which mirrors what I've seen elsewhere) is that prompt engineering can get you much farther than you may think. Most people believe they need fine-tuned models, but they really want things to "just work."

We've thought a lot about the trade offs and performance benefits of, training on models, fine tuning, etc. And what we've kind of come up with, and this was definitely also part of the advice from OpenAI, is that prompt engineering would get us there fastest in the cheapest way. And probably perform just as good, if not better than training our own models.

Besides automating grant writing, there are many potential optimizations in the climate tech space. I learned a ton about the ecosystem around government grants, and we discussed some of Streamline's roadmap beyond just "AI for grant writing."

You don't get the money when you win a grant: you have to report on your progress, file all your receipts. After you spend money, you have to get reimbursed. What this means startups is that they have to go get a loan. And that's silly. There's no way the government is going to change this process because of their own risk, but it means that every single company who's winning these grants needs to go get a working capital loan. There's people who provide, specific financing vehicles for this, and we can easily play matchmaker [between grant recipients and financing companies].

Mission-driven founders have to strike a balance between mission and monetization. Helena and I talked about balancing prioritizing the mission with dealing with the incentives inherent in taking VC funding and pursuing growth as a for-profit startup.

I've heard people refer to this as missionary versus mercenary type founders. Missionary founders, they're obsessed with whatever mission they're on and they don't really care about anything else. Mercenary founders are really just driven by "I'm going to build a bit of business that like, It goes to the moon."

And that is how most of, I think, the Bay area operates. And it leads to pretty decent business outcomes in one way. But I realized that that is never going to be enough for me. I would so much rather run out of money and keep grinding on something that I care deeply about t han pivot into something that's going to be profit generating and maximize returns.

I think I learned that when I was working on my first company, Glimpse, where we were working on a video chat company during the pandemic. A pretty safe bet. And I care very deeply about what we started on, which was helping to connect communities of people having deep conversations. It then turned into helping connect remote teams. And the further we got, the more transactional it felt. And I found it really hard.

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