While uncertain, all signs point to a prolonged period of significantly reduced funding for science. Even if the current administration were to disappear in four years, funding agencies move slowly in the best of times, and substantial damage has been inflicted on their ability to accomplish even basic functions.

The consequences of this funding crisis are causing incalculable harms; nothing in this post is going to change that. I have already seen the damage from so many talented trainees having their career plans disrupted or choosing to go to Canada or the U.K. for their graduate training. It’s infuriating to think about the scale of waste in an entire nation abandoning investments in its most talented and dedicated youth who represent its future innovation potential. Longer term, I fear for this country. Scientific advancements are at the core of our economic, political, and military might, and we are abandoning the people and institutions that generate this progress.

This challenging situation necessitates a re-evaluation of our research strategies to consider what can be accomplished within these new constraints and how, both to minimize harm and take advantage of the situation wherever possible. Massive disruptions usually mean that you might need to rethink your overall strategy and plans. This situation is no different. Maintaining scientific progress will be as important as ever so we continue to address critical problems.

This situation has led me to ponder what sort of questions I can still pursue with much less funding; I think that the answer, at least in my case, is quite a lot. Many of the most impactful scientific breakthroughs have come through the cheapest, simplest experiments, and an immense privilege of my current position is that funding almost exclusively supports students and laboratory materials, not my salary. Moreover, we are living with an absolute deluge of data from omics and large-scale studies that few ever seem to have time to revisit, and I think there are many opportunities to use these data for discovery¹. My day-to-day tasks would change quite a lot: I would do all the experiments, data analysis, and paper writing myself. My lab’s work would rely more heavily on reanalysis of existing data, with a key experiment mixed in. However, would our intellectual impact—the novelty of the findings, the influence on subsequent research, the creation of new knowledge—be reduced? I am not sure it would².

I also wonder what has been lost by the chase for funding. As graduate students, my peers and I had only a vague understanding of how our work was funded. This left us with the freedom to consider what might be possible, without putting a dollar amount next to ideas from the start. As I have gotten more senior, I have seen these big ideas get mashed into the specific aims of proposals, and discussions even start from the perspective of what the funder would want. The cost of chasing funding has been so immense, both in the time committed to writing proposal after proposal, and in limiting the bounds of what is possible to what is fundable. As the fraction of funded proposals has gone from 25%, to 10%, to single percentage points, the cost of time per funding dollar has exploded.

This has really got me thinking lately. If there is a version of a lab with the same impact using less funding, why shouldn’t I choose this approach? There are several potential downsides I can imagine:

1. Under this change, our lab would have far fewer trainees. University research both serves to uncover new knowledge and train students. This may be good or bad³.
2. I anticipate that the intellectual impact of our work would be the same, but I am not sure this would be the perception of my field. For better or worse, ideas in biomedical research are often judged by simplistic rules which can sometimes overshadow the intellectual rigor or novelty of the work. Is there an in vivo model? This is just a reanalysis of existing data. These responses do impact the perceived value of our work. At the same time, fighting for funding is often a big part of why we are forced to conform to these rules. Maybe there is value in being freed from these constraints.
3. There are questions that we currently can ask through larger-scale experiments, that would become inaccessible. However, there are also questions that I can’t pursue right now, as a result of my time and focus being pulled away to fund and support a larger group. We might lose the ability to conduct large-scale screening projects, but gain the capacity for deep, focused theoretical work or the development of novel computational methods.

In the end, we will have to choose how to adapt in this moment, and none of us can predict exactly what the future will bring.

A delightful new ecosystem of open source productivity tools has taken shape over the last few years. Just in the last year, I’ve found myself using Quarto and Typst extensively for typesetting letters of recommendation, grants, and lecture slides. Separating content from formatting pays dividends in complex documents, and the time to learn these tools was a rewarding investment. I’ve also found aider and Simon Wilson’s LLM, with Llama, extremely useful for learning new syntax and tackling more complex tasks. For instance, I recently rebuilt my CV and lab website to pull in the list of trainees, awards, grants, and papers all from shared YAML files. I am mostly unfamiliar with the Jekyll (Ruby) and Typst syntax, but an LLM could get me 99% of the way there. This means that the upfront cost of these more powerful tools is greatly reduced.