[Excerpt from https://davidlang.substack.com/p/costs 
I am not the author. David Lang is.]

My commentary below:


What should science cost?

Hacking the economics of scientific equipment.

David Lang

"Direct costs dictate research directions.

For personnel costs, the influence is straightforward. Grant availability can push more principal investigators to undertake a problem or enable the hiring of additional post-doctoral researchers in a lab.

The costs of tools are just as important, but much more difficult to pin down. Scientific equipment is an essential part of discovery. New technologies are enablers of new ideas and perspectives, and vice versa. Throughout history and across disciplines — from telescopes to microscopes, synchronized clocks to automated genomic sequencers — technology sets the pace for knowledge and insight. It's personal for scientists, too. Access to cutting-edge tools can make or break careers by enabling priority in experimentation and, in turn, earlier publication. 

As with personnel costs, tool costs dictate research directions, whether that’s determining the size of a telescope to build or deciding what kind of mice to use. Cost is the driving factor in deciding which equipment a lab will buy, share, or just leave on their wishlist. Relatedly, costs affect the pace of discovery. For example, the dropping costs of genetic sequencing have created an explosion of new research. When costs go down, we see a direct correlation with scientific output as well as industrial and commercial applications.

Analyzing the cost of tools is harder than just looking up prices on Amazon. The metascientists have approached the issue, but haven’t directly engaged. 

Kanjun Qui and Michael Nielsen laid out a metascience vision and perspective for how to improve the social processes of science. It took them two years of research to capture their important argument: we’ve only explored a small fraction of the possible arrangements for doing science. Even amongst their expansive scientific world-building, tools only got a footnote:

"It's striking that the builder of the first telescope is not remembered by most scientists, but Galileo is. The usual view is: Galileo made the scientific discoveries, but the toolbuilder did not. But they did enable discovery. This is an early example of a pattern that persists to this day. It's beyond the scope of this essay to delve deeper, but fascinating to think upon."

Paula Stephen, a leading science economist and author of the book How Economics Shapes Science, came to a similar cliff. Stephan dedicates an entire chapter to tools and materials, but there's a missing analysis of why they cost so much. Stephen points out that "despite the important role that equipment plays in research, little is known about the degree of competition in the market for equipment." 

That sums up the scientific attitude towards tool-building: forgotten footnotes. 


Back to me: 

This raises the question of how to do a research program on prices and production processes for essential equipment in, say, biochemistry. Such a program could spin off several businesses and accelerate the number of tools available to younger researchers. 

When it comes to FROs, lowering the costs of research could lower their costs significantly and liberate foundational research.

Nonetheless, equipment is likely to remain expensive and I am not optimistic that we can much decrease the price. For one, there is not huge demand pull on prices, and lowering prices through increased production will not stimulate demand. For two, in any science budget of sufficient size, saving even 20% on equipment will not change total costs by a similar percent. Increased supply is probably most helpful for educational reasons, but returns on that front wouldn't be realized, if at all, for many years.

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I would agree with you about growing costs for equipment in trendy "big science" (dark mater, hot fusion, gravity waves, accelerators), and I see this trend in military domain, like in IT in old-fashioned companies, like in nuclear industry... It is aggravated by a growing increase of regulation.
It seems that some domains push providers to improves performances to the point nobody can buy the product, but it is really perfect. I've heard that for nuclear reactors (they can resist to anything, but nobody can afford them), for tanks (they are smart, agile, powerful, robust), for military drones (they can work in civil air zone, do any mission, transport much, but cost like a helicopter)...

Meanwhile, in IT I've seen the trend to RAID disks, to Cloud, to SaaS, while Ukraine war showed the efficiency of simpler drones, not so overengineered canons, old tanks and old planes, tinkered by motivated staffs...

Here I've caught an article about the cross-pollination of AI and new experimental methods, reducing costs by 10x.

https://www.wsj.com/articles/biologists-say-deep-learning-is-revolutionizing-pace-of-innovation-eeb79c1b

It makes me think about the exponential learning curve drawn by piling S-curves... As if the end of an S curves goes to unaffordable perfect technology, and that a revolution make you start again the exponential phase of a new S-curve...

See how quickly African labs have used Crispr-CAS technology for their own needs, to fight emerging diseases or climate change in their agriculture. It was done for much cheaper than for transgenic GMOs.

Thanks for posting and considering. I agree. It would be great if more people researched this.

A simple way to start would be to study the open science hardware momentum. A few anecdotes from that scene over the past decade:

OpenROV (us) — made ROV prices >10X cheaper Open qPCR (Chai Bio) — made qPCR multiple X cheaper OpenTrons — made liquid handling multiple X cheaper Cubesats (not really OSH, but similar idea) — made satellites 10x cheaper

They are all orders of magnitude more affordable, and many have completely rearranged who uses the tool, sometimes opening up big new markets. The lack of demand pull assumption should be tested.

-David