I found this article quite interesting . Applying some kind of rigor to capital deployment.
I find that largely well-written and considered. The author didn’t get into discussion of the pitfalls of metrics like downloads. Off the top of my head, funding by downloads as a metric means:
- simple, trivial, but widely used projects that may need little updating or funding
- projects that get downloaded as separate dependencies over projects that are included within derivative works
- encouraging splitting projects just to maximize total download count (rather than as a single download)
I’m generally skeptical of algorithmic funding. I highlighted in my recent keynote at SeaGL that I think it better to set up things to encourage knowledgeable people to consciously consider where funding should go. That said, I do agree that such decisions should be guided by just the sorts of concerns the article discusses. It’s no good to just leave it to people if they decide to just go with the same unhelpful metrics. We need to emphasize understanding of which projects need the funding, use it well, and also have the most significance in the ecosystem.
I think that folks (or (large) corporations) with disposable income who want to give back need a moderately low effort way todo so.
Npm fund is one way (but only covers the specific supply chain that each developer has a view to).
An index fund of FLO is fascinating. Why not make it crowdmatched as the cherry on top?
Yes, in an ideal world, conscious awareness and fund direction would be lovely. Generally though, the folks with “cash to burn” have more time than money and want to “do something” to “give back”.
They see the FLO supply chain as a way of growing GDP and a rising tide lifts all boats.
I think they don’t want to let perfect be the enemy of good, otherwise you just have inertia and no funding.
I think snowdrift could provide a beautiful middle ground for both approaches.
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Yeah, that makes sense. And in principle, funders could have some way of discussing how to run the fund, when and how to update it etc.
You meant more money than time, right?
Yes. Sorry.
Isn’t that a bit of a chicken/egg/bootstrapping problem? Getting “the first million” is always the hardest. First money in starts the wave.
In my mind, an index/algorithmic model helps splash cash around and de-risks a bit. If some percentage squanders a bit, OK, don’t give them more. (Now of course we would then need to discuss metrics/analysis etc and that can all be gamed). It’s like the GDP of any country and universal basic income. Sure some “adult children” will “just play video games all day” but the vast majority of folks will use the leverage of additional cash to produce more value. (To be clear, I’m not trying to be overly capitalistic here. Just pointing out my view of reality). Also anyone who knows me well, knows that I believe that we haven’t ever seen true capitalism, just corporatism/chrony capitalism. The “free market” is heavily weighted by the invisible hand (and of course the well known meme of the invisible hand flipping everyone off).
I think, that crowdmatching is the only way to democratize funding. I say this as someone who is pursuing dilutive/non-dilutive funding (equity grant to VC/SBIR) and also an emerging content creator and also trying to get income from rental/SAAS businesses. I am trying all of the models and building/documenting/metric sharing in public (2025 business plan in draft with my board and will be up on TSYS discourse soon) .
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Yeah, I agree with all that. Algorithms can be a foundation from which things get evaluated and tweaked over time. I think that’s key. Don’t give all the funds up-front, make it regularly, reliable funding, and adjust things.
So, this fits a general view of algorithms having a place in a system that can still be driven by conscious human decisions. I think my concerns are largely about the direction where people take the algorithms as self-justifying. For example, I’ve seen so many cases of people using numbers-of-patents as the metric for judging innovation across companies or countries, and they never seem to ask the questions about the quality of that metric or its perverse incentives or whether the focus on patenting stifles innovation. But as long as people are adequately critical of the algorithms and metrics, I totally see how they have utility.