Graduate students are the labor in academic science and engineering

People giving out grants try to derisk them as much as possible

Manufacturing is important and often ignored

The word ‘novel’ is used as an idea bludgeon in academia

All the incentives in research drive towards conservatism

Peer review doesn’t catch duplications

Bell Labs is one of the few examples of an innovation org that was aligned with its money factory

A new professor needs about $1.5m to get going

Academic culture prizes individual recognition

Younger professors have to spend more time getting money

Academics are incentivized to do peer-reviewed publishable work

Things that are not paper-worthy enough for academia and not product-focused enough for a startup

Pay-it-forward tithing could resolve the tension between the potential public-good and potential profit in research.

The idea that academia is for crazy exploration and companies are for low risk exploration is bullshit

Three myths about scientific peer review

A PI needs to provide evidence that they have access to a lab facility

Different axes of where research gets done in the US

What was the real incentive for people to work on the DARPA Grand Challenge?

It is easier to have a multipoint mutation in technology than in evolution

NS Notes on Officers Techniques

Necrobotics illustrates how academia forces engineering

The boundaries between industry government and academia are fuzzy

Startups and Academia are bad at addressing structural constraints

The Architected Innov􏰂ation Institute - Engineering Scientific Revolutions

There are very few senior individual contributors in academia

Is it more impactful to find and train undiscovered diamonds or to unlock well-positioned but constrained researchers?

Wolfgang Merkt conversation 12 Oct 2021

An institution riffing on the ARPA Model could enable paradigm shifts that are not happening

Academic administrators and professors are playing very different games

An Institute for Creative Technology - Riffing on ARPA

Funding structures lead to several layers of indirection on what even gets proposed

Is it possible that we’re actually getting less research because we have more researchers?

Running list of relevant science news?

# §Academia Constraints
- [[Academia is the game where you gain status by getting attention for new knowledge]]

- [[Universities have multiple conflicting missions]]
- [[Universities have been pushed more towards applied research since the end of WWII]]
- [[Academic credit is a first-to-the post system]]
-  [[Graduate students are the labor in academic science and engineering]]
- [[People giving out grants try to derisk them as much as possible]]
- [[Professors do three or more jobs]]
- [[Successful professors are not necessarily the best researchers]]
- [[University professorships are the highest status jobs in academia]]
- [[National labs and other government research centers are a research alternative to universities]]
- [[The game in national labs is roughly the same as academia but with different constraints]]
- [[In academia more grant money leads to more power]]
- [[Academic research is often just the concept half of creating a heuretic]]
- [[Professors are constantly doing the calculation of whether a grant is worth the time to apply for it]]
- [[Most grant money is tagged so unrestricted money is like gold for professors]]
- [[Universities can take more than half of grant money as overhead]]
- [[A PI needs to provide evidence that they have access to a lab facility]]
- [[Physical experiments are expensive]]
- [[Younger professors have to spend more time getting money]]
- [[The government grant process depends on politics and committees]]
- [[Any technology that comes out of a university needs to go through their tech transfer office]]
- [[Months of grant writing is one of the things that makes people think twice about academia]]
- [[Academia incentivizes novelty, not focus]]
- [[Academia is not a good environment for systems engineering]]
- [[Academics are incentivized to do peer-reviewed publishable work]]
- [[Going outside of the approved process can be a black mark against people in academia]]
- [[People proposing work that they have already done is a sign that feedback loops are broken]]
- [[Necrobotics illustrates how academia forces engineering]]
- [[Labs implicitly judge research through the sense of dollars per paper]]

### Related
- [[§Startup Constraints]]
- [[§Corporate R&D Constraints]]
- [[§Philanthropy and Non-Profit Constraints]]
- [[Market Failures in Science]]

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§Academia Constraints

The number of researchers has exploded

Successful professors are not necessarily the best researchers

Universities have been pushed more towards applied research since the end of WWII

In defense of academia outline

How and Why Government, Universities, and Industry Create Domestic Labor Shortages of Scientists and High-Tech Workers

Human capital theory of stagnation

Mark Johnson the Elder Conversation 4 Jan 2021

People are incentivized to work on nodes higher up the tech tree

Nobody will admit that a field is stuck

The university is being unbundled but nobody is taking on the research part

What is the history of Universities with respect to research?

Second Lieutenants at AFRL are like graduate students in universities

Research scout network

Another downside to importing graduate students to work in labs is that it makes papers less intelligible

Michael Filler Conversation 25 Aug 2022

There is a group of people who are capable interested in science and the current institutions are not working for them

# Graduate students are the labor in academic science and engineering
Modern science needs a lot of skilled labor to do not particularly exciting jobs. Pipetting, building devices, coding, running tests. Arguably they are creating a lot of value for the PIs they work for: progressing their careers, bringing grant money to the lab and the university, creating  knowledge for society. 

Grad students provide this labor much more cheaply than the value they create. Graduate students are paid around $40k a year but it’s much much less than the same smart ambitious people would be making in a real job. The explicit deal is that you get non-monetary compensation. You do get a credential at the end of grad school. The assumption is that this credential is incredibly valuable but that deal has broken down.

This system is problematic for several reasons. 
- Imagine trying to run a company building a problem that demands a ton of [[Tacit Knowledge]], but you could only hire people with almost no experience who would never stay more than five or six years. This constant retraining is an unescapable tension if your labor is students.
- The amount of money you need to support a grad student is actually quite similar to the amount of money you need to support a high-skilled employee at a company, but about half of it goes to the university for administrative overhead and student fees. The university is actually getting a student’s worth of money from grad students to “teach” them but it comes out of grants instead of undergrads pockets. 
- Most graduate students’ incentive is to publish as many high impact papers as possible and generally do work that sounds good to a faculty hiring committee. These incentives are often aligned with their professor and lab. However, if a project requires many people’s worth of work a grad student either won’t make it onto the paper or will be last in a massive list of authors which nobody wants because a finite amount of credit is distributed among all the authors. It’s also not appealing for a grad student to work on a project that won’t produce paper-worthy results on a timescale that aligns with graduating. There’s the obvious case where the timescale is longer than the grad student expects to be in grad school. The more subtle case is that the project takes a few years but is risky enough that it’s not clear that it will produce a paper-worthy result, possibly leaving a PhD student high and dry several years in. This incentive selects against long-term engineering-heavy projects. 
- Hiring committees care about potential professors being able to do novel work. [[The word ‘novel’ is used as an idea bludgeon in academia]]. This criteria means that grad students need to start doing work that stands out from work that has been done before in the lab if they want to get a faculty job. This incentive puts pressure on projects that need a lot of focus over a long time.  [[Academia incentivizes novelty, not focus]]
- Graduate students are primarily funded through grants. Grants are typically renewed on a yearly basis (with exceptions.) So even regardless of what the grad student is ok with working on, there’s a tight coupling between grad students and doing work that can hit paper-worthy milestones on a yearly basis. Additionally, funding grad students through grants effectively ties them to a specific project. Project-coupled grad students make it near impossible to shift effort away from projects that are going poorly towards projects that are going well. Imagine if a startup couldn’t abandon things that weren’t working in favor of things that were.  [[Most grant money is tagged so unrestricted money is like gold for professors]]. 
- You can’t really fire grad students or hire them whenever you need them. For the most part, new graduate students join labs at the beginning of the academic year so if a project needs a lot of labor halfway through the year you either need to plan that out and have a grad student underutilized for half a year or you need to postpone that project until the beginning of the next year. Once you’ve taken on a grad student you’re stuck with them until they graduate or give up - this is a terrible situation for both sides.

There are several theories about how this system came about. Like any [[Complex System]], the real answer is probably “a bit of each of them.”
- It is easier to get grants (especially government ones) that are primarily earmarked for grad students as opposed to lab technicians or labor-saving automation. Why? Some combination of tradition, incentives on the part of the university because they get more fees from grad students than other spending, and the idea that money towards grad students is building up the country’s STEM workforce. 
- [[How and Why Government, Universities, and Industry Create Domestic Labor Shortages of Scientists and High-Tech Workers]] actually argues that the system was set up on purpose to keep wages for high skilled scientific workers low. 
	- Universities get more money when professors bring in larger grants. Because its easier to get big grants for grad-student-heavy projects universities are incentivized to hire professors who do more grad-student-heavy projects, perpetuating the cycle. [[In academia more grant money leads to more power]]. 

### Related
- [[§Academia Constraints]]
- 
- [[Stagnation]]
- [[bhattacharyaStagnationScientificIncentives2020]]
- [[The NSF earmarks a lot of money for training]]



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Problems where it is easy to know whether an answer is correct but hard to get to that answer

Designed Serendipity

§Augmented Knowledge Generation

Designed serendipity program

Collective Intelligence

# Reinventing Discovery
- More than a dozen Polymath-like projects have been launched  (p2)
- Description of the “postdoc” problem (p8)
- Case study: Kasparov vs the World (p17)
- [[Innocentive]] case study (p23)
	- $20,000 from Rockefeller foundation to create a solar powered router
- Expert [[Attention is the ultimate scarce resource]] (p24)
- Innocentive creates value by *Restructuring expert attention*
- [[Irina Krush]]’s  micro expertise helped the world team win (p25)
- Successful online collaborations consistently locates people with the right expertise at the right time (p27)
- Successful online collaborations have *designed Serendipity*
- The term Manufactured Serendipity came from Jon Udell
- Collaborations need to reach *collaborative critical mass*(p30)
- Summary of amplifying collective intelligence
	- Create an *architecture of attention* (p32)
		- Modularize collaboration
		- Encouraging small contributions
		- Rich and well structured information commons 
- Divisions of labor can be *static* or *dynamic* static is the typical thing in the economy but knowledge generating collaborations need more dynamic roles (p35)
- Prices are useful because they aggregate hidden knowledge (Hayek) (p37)
- Prices enable more dynamic divisions of labor
- Online collaborations work when committees don’t for two reasons (p40)
	- People in online collaborations are volunteers
	- Unconstructive people can stop a committee but be ignored in a collaboration
- Claims that you can get answers in 10 minutes from world experts (p41)
- Case study for Linux (p44)
- Cites open architecture network (p48)
- Galaxy Zoo successfully divided the big task into many smaller tasks that could be done by non-experts (p51)
- Polymath project was hard to modularize and so you actually needed full context to contribute which meant it was limited to 27 people
- Asks question - is it possible to modularize collaborations?
	- Linux
	- Wikipedia
	- Firefox
- Wikis don’t work for writing novels because every sentence could related to any other sentence (p53) 
- Predicts online collaboration tool that lets people track relationships across a collaboration (p55)
- Modularity pattern - atoms of attention -> architecture of attention (p56)
	- `Modular units are atoms of attention out of which the architecture of attention is built`
- Collaborative projects create a rich information commons that people can use pieces of (p58)
- It is hard to create tiny incremental improvements in science in the same way as on wikipedia (p59)
- Case study of Mathworks Competition (p60)
	- Try to fit songs on CD with least space left over
	- Leaderboard
	- Everybody gets to see solutions once they’re posted
- Idea of microcontirbutions (p63)
- Scores can help coordinate expert attention (p64)
- Mathworks score more effective at directing attention than polymath project where it could take hours to figure out where to go (p66)
- Stasser-Titus experiment: good and bad candidates on paper, people voted well they each had full profiles but if they had pieces of information about the candidates they consistently did not put together the full picture of them. (P71)
- Successful collaborations tend to have a gap where it is hard it is to come up with a solution easy to recognize a solution as good.  (P73)
- Stasser-Titus might have had a different result if everybody had pooled their information in the beginning (p74)
- A *shared praxis* is a body of knowledge and techniques that is shared among collaborators (p75)
- Shared praxis is critical
- A vision for how collaborative science could be done (p82)
	- Filtered requests for help 
- It’s difficult to share data in a way that’s useful for others (p109)
	- Terminology
	- Formats
	- Calibration information
	- Processing
- AI seeps into every discussion of potentially using computers to find meaning in knowledge (p111)
- `This middle-ground future is conceptually rich, fascinating, and strangely under-discussed, perhaps because the dreams of artificial intelligence exert such a strong pull on the imaginations of the technologically curious.`
- Term *Data Driven Intelligence* to describe ability of computers to pull meaning from Data (p112)
- Potential of databases to be linked up (p121)
- In science, models and explanations are blurred (p127)
- Story about someone searching for papers about a cancer as argument for open access (p159)
- Cites Pharyngula blog as getting 100,000 visits per day (p167)
- Promising but failed tools for scientists (p176)
	- Science wikis - Caltech guy tried to create quantum wiki but people all wanted someone else to take lead to contribute. 
		- No incentive to contribute except to vanity pages
		- Competition to get faculty jobs is brutal so the grad students and post docs who would be best able to contribute to a wiki don’t have incentive to contribute (p178)
	- Open comment sites
		- People wanted to read comments but not put them (p179)
- Call moving information out of people’s heads and laboratories onto a network. (P183)
- Argues that Bayh-Dole act shifted incentives towards making research more secretive (p184)
	- [[The relationship of government, academia, and technology fundamentally changed in 1980 with the Bayh-Dole act]]
	- Bayh-Dole made it so governments and grant agencies see the pursuit of intellectual property as a reason to support basic research
- Case study of Tobias Osborne trying to do open science as part of graduate school and concluding it was too much work (p187)
- Attempted contributions by people outside the praxis can overwhelm people (p199)
	- Open systems without requiring that all participants get equal attention

## Questions
- Have any polymath like projects succeeded outside of math?
- When do prizes work well? When don’t they work well
- [[How do you structure attention so that you connect to the right microexpertise at the right time?]]
- How do you apply prices to attention?
	- Especially when people are volunteers
- Did open architecture network actually work? 
- Did p55 prediction play out?
- How do you create micro contributions outside of software?
- How can you create something like a “score” for a much more open ended problem?
- Why have we failed to execute on the vision of linking databases up?
- How many visits does Pharyngula get per day in 2020? (Hypothesis that blog readership has gone down)
- Does the Bayh-Dole example push back on the philosophical argument that trying to capture all value created is optimal?
- How do you create an open-but-filtered program that doesn’t fall prey to heuristics like followers, citations - status symbols?
- Don’t all heuristics for filtering become status symbols?

## Thoughts
- Is there something to the fact that the collaboration-type projects don’t require manipulating any atoms?
- Where are [[Irina Krush]] ’s essays online?
- [[Restructuring expert attention is powerful and done poorly]]
- Collaborative critical mass is important but then there can be too many people as well - maybe the way it works is that there’s an optimum and technology can shift that optimum based on the problem
- You can’t actually get answers quickly from experts - why how could we change that
	- Need ‘spam’ filter for requests 
- You need human expertise and context glue to go from information to useful things when it’s not directly on a computer - this is why open source tends to work but you don’t see much open architecture or hardware.
- Note that in 2020 Wikipedia is having trouble getting enough people to contribute - people want to volunteer for exciting things but not boring things like maintenance.
- Seems like many open source projects are not “novel” there were operating systems before linux, browsers before Mozilla - the end goal was well known
- Rich information commons turn into a search problem and a comprehension problem. Also require a lot of work on the part of the people putting them out
- Scores need the problem to be well-defined
- Problems are often not well-defined on the knowledge frontier 
	- [[Creating tight problem definitions is valuable]]
- Kaggle is a logical extension of the Mathworks competition
- Score design is a lot like objective design for reenforcement learning
- Stasser-Titus and polymath point to the fact that tools to create shared context are super important and poorly done
- At the core of the vision for collaborative science is an [[Expertise Finding (wikipedia)]] problem
- Purely data-driven models like deep learning are not good explanations
- Pushback to value of open access - even if you have access to all the papers in the world on a type of cancer you still might have no idea what’s going on 
- Some fuzzy notion of the combination of 
	- Umwelt from [[The Intellectual Wild South Is Open For Business - Eric Weinstein]]
	- [[Bridge Model of Knowledge]]
	- And context
- Call for extreme openness in science feels a bit like socialism - it sounds really good but is in practice untenable - “hasn’t been done right”
- It feels like most semi-open online communities are overwhelmed by attempted contributions by people outside the praxis 
- StackOverflow may do the “open without equal attention” well and 
- Need better ways of filtering attention

## Related
- [[Universities have been pushed more towards applied research since the end of WWII]]
- [[Bret Weinstein - The Prediction and the DISC - Portal Podcast]]
	- Specifically the grad school problem 
- [[Irina Krush]]
	- Specifically her essays 
- Cites [[bushWeMayThink1945]]
- Cites [[Designing Organizations for an information rich world]]
- Cites [[The Attention Economy and the Net]]
- Cites [[eScience: A Transformed Scientific Method]]
- Cites [[Sam's encounter with manufactured serendipity]]

## Published
2012
## Authors
- [[Michael Nielsen characterizes institution building as making previously illegible things legible]]
## Link
https://www.amazon.com/Reinventing-Discovery-New-Networked-Science/dp/0691160198

[Web URL for this note](http://notes.benjaminreinhardt.com/Reinventing_Discovery)

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Phenomena-based cycles

Low Hanging fruit theory of stagnation

Regulations slow down technological advancement

Historical narrative is not scientific inquiry or engineering design but it is valuable

Regulations create paradigm lock-in

The peer review and citation system incentivizes people to work on things that other people think is interesting

Shareholder revolution caused public companies to focus on short-term profits

Conquest state of nature

Phenomena-based cycles are stuck

Competition and lack of slack  creates specialization

Systemic decay theory of stagnation

Exponential hardware does not lead to exponential software

Progress getting exponentially harder should be the null hypothesis

Disambiguating stagnation arguments

Technology has enabled progress to be a net positive

Regulations have increased by an order of magnitude

Does the nature of the activities to create new technology and science change over time?

Are we actually living in a world where humanity is retreating into a world of digital imagination?

Contemporary research has multiple failure modes

Why is there so little slack in mission-focused research?

The possible upsides of a healthy program design discipline seem like they could be huge

Missing productivity thread - BalajiS

The term moonshot is romantic bullshit

Is Science Hitting a Wall?

Of Flying Cars and the Declining Rate of Profit

Research success in the first half of the 20th century led to many more people becoming researchers, which changed the nature of research in the second half of the 20th century

There are private companies that do planning on the timescale of decades

Vague language only works when dealing with competent people

What if the thoughts we think are nudged by our mediums of communication

# Stagnation
### List of as atomic as possible stagnation theories
1. [[Low Hanging fruit theory of stagnation]]
	- Specifically: [[Progress getting exponentially harder should be the null hypothesis]]
2. [[Regulations slow down technological advancement]]
3. [[Universities have been pushed more towards applied research since the end of WWII]] which then means that academics have been pushed away from more long-term and genera work 
	- This could be attributed to the peer review and citation system
 4. [[The peer review and citation system incentivizes people to work on things that other people think is interesting]] 
	 - This could be attributed to more government funding
	 - This could be attributed to 
 5. [[Embedded growth obligations have shifted the cultural narrative to the idea that everything can and needs to be a high-return investment]]
 6. [[Exponential hardware does not lead to exponential software]]
 7. [[Knowledge of how markets and innovation work has made us jaded]]
 8. [[Research has become a commodity]]
 9. [[The number of researchers has exploded]]
 10. [[Successful professors are not necessarily the best researchers]]
 11. [[Specialization in research and careers in general has cut off a lot of possible innovations]]
	

* What is the rate of discovering new S-curves? How has that changed over time?
* Are today’s S-curves, individually, developing faster or slower than before?
* Are the steep parts as steep as they used to be?
* Are they plateauing at a higher or lower level?
* What is the distribution of these characteristics, over the set of all S-curves?
From 
https://rootsofprogress.org/teasing-apart-the-s-curves
- [[Stagnation and Scientific Incentives]]
- [[Stagnation and Scientific Incentives argues against the low hanging fruit hypothesis by historical analogy]]
- [[Low Hanging fruit theory of stagnation]]
- [[There are three main theories of stagnation]]
- [[Are Ideas Getting Harder to Find?]]
- [[Notes on Are Ideas Getting Harder to Find?]]



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There are three main theories of stagnation

# Systemic decay theory of stagnation
The systemic decay theory of stagnation is a loose collection of assertions that changes in institutions - government, universities, culture - are responsible for [[Stagnation]].

[[storrshallWhereMyFlying2021]] points to ever-increasing regulation as the knife in the throat of innovation. Regulation doesn’t need to explicitly ban innovations - it just needs to make the hurdles they have to clear to be adopted so high that they never make it out of the lab/prototype stage.  [[Regulations create paradigm lock-in]], and outside of economic metrics ([[collisonScienceGettingLess2018]]) the nebulous but real evidence for stagnation is that we are not seeing as many paradigm shifts in atom-based technology. Additionally, there are several technologies we can point to that are not commonplace primarily because of regulations (nuclear power, most genetically modified things, many helpful drugs) so how many *don’t* we know about. [[Arguably cars wouldn’t exist under the current regulatory regime]]. 

Some people blame the fact that [[Universities have been pushed more towards applied research since the end of WWII]] at least in part because of the tight coupling between government funding and academia. Most politicians see government spending on research as an investment  and applied research has a much clearer ROI. I find this argument a little weak because lots of non-applied research still happens in universities. Yes, a smaller fraction is unfettered discovery, but I haven’t seen any compelling reason to believe that the fraction matters.

While the composition of university research might not be to blame, there are a lot of theories that center around the coupled dependence on and mismanagement of government funding for research. Everything from risk aversion ([[Evaluating transformative research programmes: A case study of the NSF Small Grants for Exploratory Research programme]]) to the bureaucratic apparatus that makes it extremely painful to apply for grants ([[Giving Away Money Costs More Than You Think]]), for the most part only gives small or absolutely huge grants. [[storrshallWhereMyFlying2021]] goes so far as to point out that GDP growth is in fact inversely correlated with non-military government R&D funding. Most foundations are not much better. 

One contributing factor might be the death of corporate R&D. We can point to so many impactful innovations that came out of corporate R&D in the middle of the 20th century. [[aroraChangingStructureAmerican2020]] notes that incumbent companies have shifted towards expanding markets by mergers and acquisitions.[^1] This strategy shift means that most innovations need to either work within all the constraints of a startup ([[§Startup Constraints]]). 

There’s a mini-nebula of theories around [[The (idea) valley of death]].  What would ideally be a communication-rich channel going both ways between people trying to innovate and improve on existing technologies and the people who use and sell them is instead a high friction, leaky pipeline with very little feedback. [[Academic research is often just the concept half of creating a heuretic]], but a proof of concept is often not enough to derisk a for-profit company.  [[drexlerRadicalAbundanceHow2013]]  argues that many innovations don’t happen because  [[Academia is not a good environment for systems engineering]], which is necessary and underrated for building actual functional products.  There is another class of theories that the valley of death has widened because globalization and other forces have separated manufacturing from research. [[Definite Optimism as Human Capital]]. 

The discussions around The Valley of Death invariably touch on the patent system. These theories go that [[The relationship of government, academia, and technology fundamentally changed in 1980 with the Bayh-Dole act]]. Now technology transfer offices are all trying to grab their share of the profit (despite the fact that [[Most tech transfer offices are not profitable]]), making it hard to combine patents into impactful innovations, demanding a large enough share in companies that it makes it hard for some to get funded, and generally increasing the friction to turn university research into useful products. [[Any technology that comes out of a university needs to go through their tech transfer office]]. Corporations also abuse the patent system - taking out defensive patents that mean paradigm shifts that require several new pieces are all potential lawsuits. 

The patent issues point to a more [[Nebulous]] systemic problem: declining [[Slack - concept]] across institutions: Academia, Incumbent Corporations, and Startups. This idea is a massive rabbit hole so I will touch on it only briefly, but in general [[Systems must take performance hits to get out of local optima]] but without enough slack, any system (or individual) that takes a performance hit will get competed out of existence. The oversaturation of academia with people, potential returns to investing in software, patent obsession, regulations, and decline of R&D labs may all have contributed to this declining slack.[^2] 

Declining slack is coupled to an *even more* [[Nebulous]] issue: culture. In part, the world is so competitive because the pie doesn’t feel like it’s growing anymore in the first place, so it’s unclear which way the causality runs between [[Stagnation]] and culture. Peter Thiel’s critique is mostly a cultural one ([[Back to the Future (Thiel)]]) that we don’t make and support big, precise plans ([[Definiteness means that you have a long-term plan]] and generally don’t have a definite optimistic stance, embracing indefinite optimism which leads to incremental improvements and short-term optimization. [[To make new things you need to take risks]] and there’s a sense that we are just more risk averse (see the previous paragraph about regulation.) Perhaps related is the phenomena of increasing leader age  across institutions. And there’s a sense that with the decline of religion and nationalism we aren’t striving for anything bigger than ourselves or our lifetimes. 

### Relationship to other stagnation arguments

The systemic decay theory is almost the dual of the [[Human capital theory of stagnation]], which stands to reason: systems both are composed of individuals and drive their incentives. The difference may be in the flavor of solutions that each theory suggests - whether you focus on shifting individual incentives or institution building/reform. Even then, the lines are blurry.

All of these varied causes could be *why* [[Low Hanging fruit theory of stagnation]] is correct. Perhaps there is a limit to how coordinated humans can be and we’ve picked most of the fruit that can be easily grasped within those constraints. Similarly, all of these effects could be *because* we’ve run out magical growth pixie dust. 

### Counterarguments
I need to do more work to understand the counterarguments more deeply. They boil down to the fact that as a society we *are* spending a lot of money and manpower on research. It’s not like *nothing* new is being created, and we’ve tried a lot of things that just haven’t worked. 

(A situation in which you’re pouring in effort but nothing is coming out sounds consistent with massive friction: [[Our ability to do things in atomland is dying a death by 1000 cuts]])

### Related
- [[Notes on the changing structure of American Innovation]]
- [[What changed from the high-quality corporate labs in the past?]]
- [[Any technology that comes out of a university needs to go through their tech transfer office]]
- [[Low hanging fruit implicitly assumes a linear development model]]
- [[Stagnation]]
- 

### Works not cited in the body
- [[aroraChangingNatureCorporate2015]]

[^1]: [[AI research is an exception to the decline of corporate R+D]]
[^2]: Note that I’m not saying competition is bad. Simply that more competition is not always better. [[alexanderStudiesSlack2020]].



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Stagnation

Reinventing Discovery

Universities have multiple conflicting missions

# Universities have been pushed more towards applied research since the end of WWII
Universities trend towards more applied research was accelerated by the US government both pouring money into them and putting them in charge of large military research projects

This needs to be tightened up

## Related
- [[Balaji S. Srinivasan on Twitter: “Fund the individual to get out of grad school.… “]]
- [[weinsteinBretWeinsteinPrediction]]
	- Specifically the description of the grad school problem [[Graduate students are the labor in academic science and engineering]]



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Universities have been pushed more towards applied research since the end of WWII

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