Stagnation

The decline of unfettered research

Stagnation and Scientific Incentives

Is Science Slowing Down? - SSC

Science is getting less Bang for its Buck

Is the rate of scientific progress slowing down?

Metrics don’t distinguish between Type I and Type II progress

There is an increasingly wide gap between the number of graduate students training for PhDs and academic positions for them to fill

Expanded list of WTF happened in 1971 and afterwards

Stagnation depends on what you’re measuring and how you slice disciplines

Notes on the decline of unfettered research

Martin Permin conversation 9 Sep 2021

Azoulay Economics of Ideas Syllabus

# Are Ideas Getting Harder to Find?
- `Economic growth = Research productivity × Number of researchers `
- `The aggregate evidence, then, may tell us nothing about research productivity at the micro level. Hence, the contribution of this paper: study the idea production function at the micro level to see directly what is happening to research productivity there.`
-` What if there is sharply declining research productivity *within*each product line, but growth is sustained by the cre- ation of new product lines? First there was steam power, then electric power, then the internal combustion engine, then the semiconductor, then gene editing, and so on. Maybe there is limited opportunity within each area for productivity improve- ment and long-run growth occurs through the invention of entirely new areas. `
Original Romer Model ([[The Origins of Endogenous Growth]]) assumes no productivity within product lines 
`a constant number of researchers can sustain exponential growth if the variety- discovery function exhibits constant research productivity ` ::Wait wouldn’t that require researchers to create new disciplines at a constant rate?::
But TFP is going down 
![](BearImages/E0B5B352-A238-4B05-9C19-1D68F8299BCA-1821-000468A5362D94AF/B5AF8134-460A-4EFB-AD00-29731744B54F.png)
![](BearImages/407FAA9C-1C09-4289-859A-500C0FD6C22B-1821-000469517D32E37C/BFAF078D-43AA-4E2F-B0E7-0D45ED694718.png)
![](BearImages/83499ACD-3F09-4192-A4F4-EF8314D0DAD1-1821-00046958CF07C945/3BB62AB9-774C-4250-921B-F998FD5A3C14.png)
AGRI

### Published
2020
## Questions
## Thoughts
## Related
- [[Is the rate of scientific progress slowing down?]]
- [[Science is getting less Bang for its Buck]]
- [[Is Science Slowing Down? - SSC]]
- [[Stagnation depends on what you’re measuring and how you slice disciplines]]

## Authors
- [[Nicholas Bloom]]
- [[Charles Jones]]
- [[John Van Reenen]]
- [[Michael Webb]]
## Link

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Why do we seem to be in a world where humanity is retreating into a world of digital imagination?

Axiom - A world with more growth and wonder would be preferable to the one we have now

# Are we actually living in a world where humanity is retreating into a world of digital imagination?
If we aren’t, the question [[Why do we seem to be in a world where humanity is retreating into a world of digital imagination?]] is irrelevant.

### Related
- [[Stagnation]]



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You don’t get to choose the rules of an absolute game

As a platonic ideal, research is an unscheduled quest for new knowledge and inventions with hard-to-predict outcomes

The Idea Factory

Relative games always have a winner

Frontiers drive human flourishing

I am an unabashed explorationist

Space exploration can surface new phenomena — we need to go out there and poke stuff

# Conquest state of nature
Most people know the story of how, coupled with the enlightenment, we figured out that technological growth enabled growth without bloodshed.[^1] I want to frame technological growth as a conquest of nature and make you aghast by arguing that we should embrace being that sort of conquest state. 

Historians can classify most states throughout history as either “conquest states” or “maintenance states.” The former are characterized by aggressive border expansions, financial surpluses, and warrior cultures. The latter are characterized by static or contracting borders, debts, and scholarly or decadent cultures. [^2] Throughout history, growth primarily happened via conquest.  Yes, there were gains from trade and small technology improvements ([[Smithian Growth vs Schumpetarian Growth]]) but they were the tiny minority. Land, slaves, and booty from conquered territory were the primary ways that the pie grew. Of course, a pie that grows by conquest meant a shrinking pie for someone else, so on net, this sort of growth wasn’t great for humanity. 

You could look at technological growth as a process of exploiting phenomena. [[Technological and scientific paradigms are each based around exploiting a specific phenomenon]]. What are phenomena but the territory and jewels of nature? Our ability to harness more phenomena is pushing ever deeper into Nature’s territory. For most of our history, we subsisted on Nature’s borders. Like an annoying nomadic tribe on the border of the Roman or Han empires, we mounted an occasional raid but for the most part were not a threat and at mostly Natures mercy. Pandemics would decimate our populations, weather fluctuations would create famines, and wild animals regularly killed people. And like annoying nomadic tribes throughout history, we woke up and realized that we are powerful, bending one phenomenon after another to our will. 

Over time, various states have had varying amounts of focus on natural conquest. China in the 12th century, Britain in the 18th and 19th centuries, and Germany in the early 20th century pushed deep into natural territory. On the other hand, Japan until the mid 19th century explicitly gave up its bellicose (towards nature) ways by explicitly freezing technological development. 

Like the Ottoman Empire under Suleyman the Magnificent, the US reached its nature conquering Golden age in the 50’s and 60’s. And then, like so many conquerers, it ran out of momentum. Partially this is because Nature put up heavy resistance. Partially this is because Nature seditionists acquired more and more power. 

Now, we cower in fear at natural phenomena like global warming and pandemics, speaking of peace bargains and tribute to Nature in the hopes of being spared. Scientists and engineers continue to fight rearguard actions but we have lost the cultural will to drive forward. It does not need to be this way! 

How could I be advocating the rape of Mother Nature that we’ve only recently begun to undo?  In the past we were stupid conquerers. Yes, destroyed forests, polluted rivers, and extinct animals are the result of pillaging nature. Only stupid conquerers salt the Earth. [Smart conquerers make life better for their subjects.](https://youtu.be/uvPbj9NX0zc?t=44).  As we become more powerful, the more we can leave pristine the territory of nature that we conquer. 

As much as we might be loath to admit it, conquest states embody so many cultural aspects that we admire. Decisiveness, higher purpose, bravery, cohesiveness, curiosity, and growth. Cowboys, arctic explorers, knights, and great scientists are all the exhaust of conquest state. And at the end of the day, life is better for the average person in a conquest state than a static or declining state. Conquest leads to growth, which means that the pie is getting bigger and people can hope to have a better life than their parents and believe that their children will have a better life than they do. [[People are less worried about equality when the pie is growing]]. Conquest gives cultures shared purpose and drive. 

### Related
- https://twitter.com/elonmusk/status/1299430971494731776?s=20
- https://overcast.fm/+Jf3r-yaAk/46:12
- [[We should treat space exploration the same way people in the 13th century treated building cathedrals]]
- [[Use NASA as a never-ending Lewis and Clark expedition]]
- [[Technology has enabled progress to be a net positive]]
- [[Where’s my Flying Car - A memoir of futures past]]
- [[Stagnation]]
- [[Optimistic stagnationists]]

[^1]: People still resorted to zero sum growth (in part driven by technology as previously worthless materials became valuable resources) 
[^2]: Obviously these are gross simplifications



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Overlapping institutional constraints rule out several classes of creative work

Research requires massive intrinsic motivation

ICT Distillation

ICT Outline

# Contemporary research has multiple failure modes
When we talk about [[Stagnation]] in “research” we are lamenting some admixture of several  failure modes.[^1] 

One class of failures is in the failure to enable paradigm shifts. You can roughly divide the failure to enable paradigm shifts into two modes. The first mode is the “Einstein would be stuck in the patent office” mode:  we don’t seem to have a healthy system of unfettered research that is producing paradigm shifts like those seen up through the 50’s or 60’s. The second mode is the “Where’s my flying car?” Mode - we don’t seem to be able to build new physical systems that permeate into the world. One might roughly call these two “paradigm-shifting science” and “paradigm-shifting engineering” but the line between science and engineering is [[Nebulous]], porous, and full of feedback loops [[Research has many orthogonal and non orthogonal classification axes]]. 

Interestingly, [[The decline of unfettered research]] ties together both [[Einstein would have been stuck in a patent office - failures of paradigm-shifting science]] and  [[We have no flying cars - failures of paradigm-shifting engineering]]. 

Another class of failures is what I might call “breakdowns in the scientific process.” Here you see issues like the replication crisis and science being judged not on one of several scientific epistemologies, but on politics and its ilk.

 It’s important to call out these different modes because while they share many similarities and causes, precision in language leads to precise thoughts [[Precise Language Enables Progress]]  and the steps to address each mode are likely different. It’s also important to example the causal links between them, which might not be as tight as we think despite sharing confounding variables.  

### Related
- [[It is important to precisely define problems in an area]]
- [[Precise communication enables other people to make decisions]]
- [[Phenomena-based cycles]]
- Here I am being a splitter [[Lumpers and Splitters]]

[^1]: Papers like [[Science is getting less Bang for its Buck]] lump “research”  into one category and so does most casual conversation



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Noah Smith Rebuttal to the Real Trouble With Silicon Valley

We need to take a more engineering approach to enabling more awesome shit

# Disambiguating stagnation arguments	
It ‘feels’ like things have stagnated and GDP does not seem to be growing as quickly as it once did. But what do we actually mean and where are the problems. 

[[There are three main theories of stagnation]] and for each of those arguments there are different timescales to consider. 

- If you’re looking at the 0-5 year timescale, you care about commercialization and dispersion.
- If you’re looking at the 5-15 year timescale, you care about [[The (idea) valley of death]] and Mission-oriented research. Harnessing phenomena we  roughly understand, integrating it into useful systems. This still requires a lot of research but focused on results. 
- If you’re looking at the 15+ year timescale, you care about exploring new phenomena. 
Arguably there is stagnation at each of these timescales. 

### Related
- [[Stagnation]]
- [[Periodic tables are a useful way of categorizing]]



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AI research is an exception to the decline of corporate R+D

Heilmeier — Licklider — Braben — Elon quadrants of Research Management

AT+T benefitted significantly from creating better technology

Precommercial scitech is a nebulous term for a certain class of frontier activities

# Does the nature of the activities to create new technology and science change over time?
Underlying many conversations about [[Stagnation]], science, technology, and innovation are implicit assumptions either that the nature of the activities 

### Related
- [[Activity Space]]

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§Academia Constraints

The number of researchers has exploded

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

Successful professors are not necessarily the best researchers

Human capital theory of stagnation

Mark Johnson Conversation 4 Jan 2021

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

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

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

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

Nobody will admit that a field is stuck

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]]
- [[Stagnation and Scientific Incentives]]



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Research has many orthogonal and non orthogonal classification axes

Radical Abundance

Narratives are how we deal with uncertainty

Academics want to do timeless work

By definition outlier analysis can’t rely on data as comparison to other organizations

A lot of valuable knowledge is non-scientific

The iron rule

Science as Falsification

Outlier analysis is a historical discipline

Historically things can be bad without breaking for much longer than we expect

History doesn’t teach lessons

# Historical narrative is not scientific inquiry or engineering design but it is valuable 
In [[Radical Abundance]] Drexler introduces the dichotomy between science and engineering. One of the important distinctions was that in science a theory can be wrong (or at least not quite right) because it doesn’t work a single time after a thousand successes whereas in engineering something can be right (or at least possible) if it works once after a thousand failures. 

I would propose “Historical Narration” as the third class of pursuit. Unlike science, history will *never* have even a ‘most correct’ theory. Unlike engineering, history will never have a concrete system that either works or doesn’t. And yet, history is a valuable pursuit. Its different narratives provide patterns for us to match against and lenses to see what’s going on in the world. [[Historians may be the most nuanced field because they know there can never be a right answer]]. Perhaps one day, we’ll have historical science ([[Cliodynamics is trying to be psychohistory]]) but it’s important to realize that narrative is an important pursuit *without* it being design or inquiry. 

It is important to call out narration as a third and distinct pursuit from inquiry or design because doing one pursuit and framing it as another leads to bad results. As [[Eric Drexler]] points out, doing engineering while thinking about it like science leads to never actually making something useful. Similarly, many parts of economics are actually narration but pretend that they are some combination of inquiry and design. I suspect this is what’s happening with various [[Stagnation]] hypotheses. There is never going to be a grand theory of why innovation seems to have stagnated, nor even whether it has stagnated. It’s fine to continue to create and refine narratives, but treating it like science (ah! We will find *the* reason) or engineering (I can’t create a design to fix it until I diagnose the root cause) is foolish. 

In a way, Math is some kind of extreme version of science - it’s simply correct theories abstracted away from reality. Analogously, Philosophy is an extreme version of history abstracted away from reality. What is the engineering version of this? Perhaps because information in engineering flows *towards* reality, there is no analogy. Maybe it’s science fiction - designs abstracted away from reality. Then you could draw a parallel between Scientific Inquiry, Engineering Design, Historical Narration to Math as Inquiry abstracted from reality, Science fiction as design abstracted from reality, and philosophy as narration abstracted from reality. 🤔 That doesn’t feel quite right. 

Another thought is that many disciplines are actually hybrids between the three pursuits. Trying to create a new organization resembles engineering design *driven* by historical narrative. A lot of outlier analysis resembles some mix of historical narrative and scientific inquiry, where the narrative is the evidence that’s used to prove or disprove the theory. History itself can be more narrative driven or more inquiry driven. Like so many things, these distinctions are [[Nebulous]] and want to be axes rather than boxes. 

### Related
- [[Research has many orthogonal and non orthogonal classification axes]]
- [[People spend too much time on scientific inquiry and historical narrative building in the realm of enabling more awesome]]
- [[Fiction / Science Fiction/ Historical Fiction are a way of exploring counterfactuals]]
- [[It is possible to X is engineering, it is impossible to X is science, here is a narrative around X is history]]
- [[A lot of valuable knowledge is non-scientific]]
- [[History as a braided rope]]
- Is there something analogous to[[The iron rule]] for history? 

### References
- 



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Low Hanging fruit theory of stagnation

Systemic decay theory of stagnation

There are three main theories of stagnation

# Human capital theory of stagnation
The human capital theory of stagnation is that specialization among researchers and the incentives they experience have increased coordination costs and led to effort allocation that has decreased the rate of breakthroughs.  

We do know that specialization and division of labor drives Smithian growth. ([[Smithian Growth vs Schumpetarian Growth]]) The scientific economy is subject to those same forces and researchers have become increasingly specialized. However, this specialization may be contributing to [[Stagnation]] in several ways. 

One argument goes that since [[New ideas need to come out of a single mind]] and new innovations don’t actually require fewer knowledge areas than they did in the past. Combined with specialization, this requirement means that innovations need increasingly large teams of people working closely, which leads to higher coordination costs per invention in order for each specialist to understand the others enough to contribute usefully.  Both [[The Burden of Knowledge and the ‘Death of the Renaissance Man’ Is Innovation Getting Harder?]] and [[The decline of unfettered research]] hint at this argument. 

Another fact is that the number of researchers has exploded along with the specialization. However,  despite the internet, the explosion of researcher numbers and specializations means that their ability to find the right person to collaborate with has not kept up. So while there might exist a productive division of labor on a problem, it takes a ton of effort to *find* the person to collaborate with.  [[The Economics of Scientific Collaboration]] touches on this theory. 

[[The decline of unfettered research]] argues that the exploding number of researchers means that they just *can’t* operate a mode they did in the past. The explosion of research has led innovations becoming effectively a commodity and expectations of continuously improving systems. These outcomes in turn both make scientists fungible and expected to churn out improvement at a certain rate that need to work with existing systems. My personal take is that these incentives aren’t conducive to deep work or paradigm-shifting innovations.

Other incentives on scientists have also pushed them to work on more incremental shorter term projects. [[Stagnation and Scientific Incentives]] argues out that two of the biggest incentives driving this incrementalism are citation-obsession and grant-giving processes. The number of papers a research has published and the number of citations those papers garnered are a key metric for measuring a professor’s performance when considering tenure, hiring, and prestige. Thus, anybody playing the academic research game (which includes people in universities, national labs have incentives to publish things that will get a lot of citations.  ([[Academia is the game where you gain status by getting attention for new knowledge]]. ) Keep in mind that these citation incentives also include National Labs ([[The game in national labs is roughly the same as academia but with different constraints]]) and many people in corporate R&D labs, as papers are the primary shared currency between those worlds.  

In order for something to be publishable, work needs to be sufficiently novel ([[Academia incentivizes novelty, not focus]]), so individuals are pushed away from grungy engineering efforts to implement already-described ideas. A publication also needs to get through peer review ([[Academics are incentivized to do peer-reviewed publishable work]]) which punishes excessive divergence from current paradigms [[The peer review and citation system incentivizes people to work on things that other people think is interesting]].

In order to get lots of citations, a publication needs to hit a sweet spot where it is both new but not far outside of current paradigms. Of course, if you publish a paper that kicks off a new paradigm, it will get many citations. However, paradigm-shifting papers also are likely to get close to zero citations if they’re even published (note the above point about peer review filtering for ideas that are in-paradigm.) This tradeoff sets up a choice between a high risk, high reward option and a lower-risk medium reward option. The fact that there are two options doesn’t necessarily lead to stagnation, but combined with citation-obsession and the incentives of grad school, the potential downsides of the high-risk option are too great.
Remember, most academic researchers are grad students who would like to graduate and get a good job in a finite amount of time and [[There is an increasingly wide gap between the number of graduate students training for PhDs and academic positions for them to fill]]. 

The majority of grants are given out by foundations or government agencies.  In these situations, [[People giving out grants try to derisk them as much as possible]]. There is relatively little upside to funding wildly successful research, but substantial downside to wasting someone else’s money. This [[Asymmetric career risk]] for grant givers mean that researchers applying for those grants need to convince the grant giver that 

Navigating the publication-grant game is a skillset in and of itself, which means that [[Successful professors are not necessarily the best researchers]]. It’s hand-wavey, but you could see how a research world that rewards good managers more than good researchers might lead to a shift of human capital that could impact the sorts of innovations that happen. 

On top of all of this, [[Academic culture prizes individual recognition]] which is one of many contributors to [[The (idea) valley of death]] that prevents many innovations that require a bunch of grungy work to scale up from making an impact. 
	
Overall, the human capital theory of stagnation feels like stagnation by a thousand cuts. However, the uniting cause seems to be the massive growth in the number of people doing research and the resulting cascade of locally rational adaptations. If this is the case, fixing each of these issues one at a time may be like playing whack-a-mole and you definitely can’t decimate the number of people doing research.

The human capital theory of stagnation (especially the flavor in [[Stagnation and Scientific Incentives]]) is closely tied to the [[Systemic decay theory of stagnation]] because the systems drive individual’s incentives and would *ideally* be acting to attenuate individual incentives that harm the system on-net. [[Institutions are the second level of a group selection evolutionary system]].  Specifically, citation-obsession and grant-gaming are results of the bureaucratization of research. 

Arguably the [[Low Hanging fruit theory of stagnation]] could be driving the specialization because as new knowledge gets harder to find, people are forced to specialize more and more. I’m not sure if it’s possible to tease apart how much of the specialization is driven by the nature of research itself and how much is driven by sociological forces.

There’s also a general sense that (partially driven by specialization) people don’t know how to build things anymore. That is, if someone had an idea about a flying car in the 50’s he might be able to just talk to his neighbor who knew how to build cars and airplanes and they could whip together a prototype. [[Definite Optimism as Human Capital]]. 

### Counterarguments
[[Is Science Slowing Down? - SSC]] argues convincingly that logarithmic technological progress should be the null hypothesis (remember, we have no immortal god-emperor Eisenhower or immortal god-emperor Marcus Aurelius.)  In this frame, the human capital theory of stagnation is simply how [[The gods of straight lines]] are enforcing that logarithmic growth. 

A more mundane counterargument is that there is actually a lot of good science being published and funded. Sure, it could be better, but on the margin, people who would do crazy research are still doing that research and if they’re good at it, they can still find money to do it. 

### Related 
- [[The decline of unfettered research]]
- [[Graduate students are the labor in academic science and engineering]]
- [[One Process - Post]]



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# Is Science Hitting a Wall?
Is Science Hitting a Wall?

https://blogs.scientificamerican.com/cross-check/is-science-hitting-a-wall-part-2/

### Related
- [[Stagnation]]



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Culturally we expect modern organizations to become a monopoly

Margins and innovation have a complicated relationship

What is a market failure?

# Knowledge of how markets and innovation work has made us jaded	
Humanities collective knowledge about how markets and innovation work still has a long way to go, but we know much more than we did in the industrial revolution. It is shocking how many inventors died in poverty [1]. I suspect those people wouldn’t have even tried inventing such an ‘indefensible’ thing given the collective knowledge we now have. 
The statement “everybody knows you can’t make money in X” makes it so that nobody even tries.
[1] Reference: [[Lever of Riches]]

 [[The decline of unfettered research]] points out that we actually understand how important incremental work is. Is it possible that this makes use shift focus to more incremental work?![](BearImages/AB1621BD-3581-406F-ACFA-64B55D833160-19889-0004B3D35FA551E0/A965FDC6-1AF0-4D8C-B22C-591CDCB7C32A.png)


            

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Scientific Freedom - The Elixir of Civilization

Where’s my Flying Car - A memoir of futures past

The Burden of Knowledge and the ‘Death of the Renaissance Man’ Is Innovation Getting Harder?

Bridge Model of Knowledge

Healthy corporate R+D requires a trifecta of conditions

Technological and scientific paradigms are each based around exploiting a specific phenomenon

Stagnation and Scientific Incentives argues against the low hanging fruit hypothesis by historical analogy

Science has become more capital and labor intensive over time

Corporate labs no longer fill the niche they did in the early-mid twentieth century

Notes on making ambitious awesome things 15 Aug 2020

Technology doesn’t solve everything but it does generate growth which arguably does solve everything

In world of low growth people's opinions matter more

Notes on the changing structure of American Innovation

If logarithmic growth is the default, it should be even more imperative to find new avenues of growth instead of just exploiting the ones we have and riding waves

Important SSC posts

Is it possible that the regulatory technological overhang reached back and killed R&D

Notes that get screwed up somewhere in the pipeline

Strong Inference

You once got to affect the world with your cleverness

# Low Hanging fruit theory of stagnation	
# Low Hanging fruit theory of stagnation	
The low hanging fruit theory of stagnation is based on the assertion that there is a finite amount of knowledge to be gleaned about the universe and we have already gleaned that knowledge that can be acquired without massive teams, expensive equipment, or spending your life studying to get to the knowledge frontier ([[Ars Longa Vita Brevis - SSC]].) In this situation, knowledge is like a tree of fruit or a mine of gold. 

The evidence for this theory is straightforward. Benjamin Franklin could discover physics phenomena by flying a kite in a thunderstorm. Now a team of hundreds needs a billion dollar particle accelerator.Examples like this are legion.   [[Physical experiments are expensive]]. [[Is Science Slowing Down? - SSC]] makes the point that since [[Connections create new knowledge]] , if new knowledge was *not* increasingly hard to find, we would have an immortal god-emperor Eisenhower, if not an immortal god-emperor Marcus Aurelius. 

The low hanging fruit theory feeds into the [[Human capital theory of stagnation]] through the mechanism that reaching new fruit requires more combinations of specialized skills than before, forcing people to specialize more and form larger teams. These large teams of hyper specialized individuals create inefficiency and cost.

This note is much shorter than the descriptions of the [[Human capital theory of stagnation]] and the [[Systemic decay theory of stagnation]] because Low Hanging Fruit is just a simpler explanation. Instead of “there’s this issue on top of this issue on top of this issue” you can just say “well, there’s just less stuff - all of those explanations are [[Epicycles]].” On the one hand, simple explanations are appealing and tend to be right in physics. On the other hand, [[Simple explanations work well in physics but not in people]]. 

### Counterarguments
To abuse the analogy - there may be a difference between picking fruit and finding new trees and we may have become worse at the latter. S-curves in a discipline are definitely a thing, but it depends on how you define a discipline. So while all the low-hanging fruit in particle physics may have been picked, perhaps there an infinity of other areas we don’t even know about.  [[Jason Crawford]] touches on this in [Teasing Apart the S-Curves](https://rootsofprogress.org/teasing-apart-the-s-curves). The [[Human capital theory of stagnation]] could create this situation through extreme specialization if creating new trees requires combinations of skills and the [[Systemic decay theory of stagnation]] could create this situation if the systems that promoted new skill discovery fell apart. 

Flippantly, people have asserted that knowledge has been tapped out repeatedly throughout history. Each time they were clearly wrong, which raises the question “why is this time different?” Generally, the burden of proof should be on the theory that is asserting “this time is different” and as far as I’ve seen the low-hanging fruit theory doesn’t have a precise answer.

### Related
 - [[Stagnation]]
 - [[Structural vs Contingent Causes]]



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Fast decisions involve few people

# Missing productivity thread - BalajiS
Theses
1. The Great Distraction. All the productivity we gained has been frittered away on equal-and-opposite distractions like social media, games, etc.
2.  The Great Dissipation. The productivity has been dissipated on things like forms, compliance, process, etc.
3. The Great Divergence. The productivity is here, it’s just only harnessed by the indistractable few. (Isn’t this the same as the great distraction)
4. The Great Dilemma. The productivity has been burned in bizarre ways that require line-by-line “profiling” of everything, like this tunnel study.
5. The Great Dumbness. The productivity is here, we’ve just made dumb decisions in the West while others have harnessed it. See for example China building a train station in nine hours vs taking 100-1000X that long to upgrade a Caltrain stop.

- Aren’t we more “productive” in both an economic sense and in the sense that we produce more of the things that computers enable: words, music, software, mass-produced widgets.
	- What we aren’t producing is more good ideas or custom physical systems 
		- And why should we? 
- What are the blockers to the sorts of productivity you’re thinking of 
https://twitter.com/balajis/status/1329315470999126016?s=12

### Related
- [[Stagnation]]

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# Notes on Are Ideas Getting Harder to Find?
*Note to the reader: I wrote these notes mostly because I found the paper confusing the first two times I read it and I think it’s important to have a precise handle on different arguments around the state of technology and research.*

[Bloom, Jones, Van Reenen, and Webb ask “Are Ideas Getting Harder to Find?”](https://web.stanford.edu/~chadj/IdeaPF.pdf)

Spoiler alert: Yes. 

The paper also asks “Are scientists becoming less productive?” To which the answer is also yes. *For a very specific and non-intuitive definition of ideas.* Understanding this paper is all about grokking definitions and what it’s actually arguing about. 

In all fairness, the authors do note in the middle of page five that a better title might have been `Is exponential growth getting harder to achieve?`  But it is buried so deep it doesn’t absolve the confusion because of graphs like this, which unless you understand the embedded assumptions about ideas, amounts to “wow, scientists today suck compared to the 1930’s. I mean, Einstein and Bohr were pretty badass, but all the 1930’s scientists taken together were 30x better than 2000’s scientists?” 
![](BearImages/F294D6DD-D46B-40B9-9A76-C81C19426A14-89396-0003F03BC8FB6868/F6EC6C10-4FAD-4766-B7AC-C76D2C11C259.png)

In growth economics, an *idea* is defined as a thing that creates a constant exponential rate of growth. So by definition if the growth rate goes down in a year, the number of ideas that were created that year is also lower.  The model treats ‘scientists’ as miners for these idea, and their productivity is ideas produced per scientist per year. Intuitively, someone’s productivity is the number of widgets they can create per unit time. So measuring a scientist’s productivity in ideas per year seems … reasonable. However, because of how we’ve defined ideas if the economy’s growth rate stays constant and the number of scientists has gone up, that means that those scientists have become less productive because the number of ideas per scientist has gone down. In other words, if a fixed number of scientists cannot create not a constant number of widgets per time, but a *constant exponential growth rate* in the value of all the widgets being produced, they are becoming less productive. 

 The implied null hypothesis of endogenous growth theory is that a fixed number of constantly productive scientists should be able to output enough science to keep an economy growing exponentially at a constant rate *forever.* This is, in my opinion, absurd. 

“Hey 100 dudes and ladies doing science at the end of the 19th century - y’know if you don’t keep the economy growing at the same rate you’re slacking on the job.” 

I have very little understanding of traditional endogenous growth economics so I wonder if this is a gross misinterpretation of its null hypothesis. 

The rest of the paper makes a lot of sense if you view it through the lens of providing evidence that throughout the economy, a constant number of researchers are unable to discover and invent enough to maintain a constant exponential growth rate. That isn’t to say that most areas haven’t been able to maintain a constant exponential growth rate, though. To me, ‘ideas getting harder to find’ leads to “there are fewer ideas to be found” which would imply slowing or stagnant growth. This is the case in a few examined areas, but in many areas there’s bee a lot of progress! It just feels misleading to constantly compare constant growth *rates* to absolute numbers of researchers. 

You see this graph and think “doooooooooom!” 
![](BearImages/E74EDAEF-A785-4071-8A33-36E7589D63B0-89396-0003E588BC0596ED/83E447C0-B00E-4DB9-B4E1-307995CAC8F8.png)
But then you see this graph and it’s like “Wow, we’ve managed to almost *triple* the number of bushels of corn an acre of land can produce - that’s awesome!
![](BearImages/3CD64B6B-5438-460F-8F23-FC861140C4DC-89396-0003E57E9482818B/512EF4D3-48E8-46EC-BAA3-B7F58D8F483A.png)

I realize that it’s not completely fair to critique a paper’s emotional impact but at the end of the day we are not creatures of pure logic and subconscious optimism or pessimism really has a big impact. 

Most of the paper is devoted to hammering home the point that we can’t get exponential outputs with constant inputs. Near the end is a great table where they discuss *how fast* the inputs need to grow in order to get those exponential outputs. 
![](BearImages/71E7A8E5-5C1C-4668-946F-D5B3732C80E4-89396-0003E62EA5BE2677/0AD42A2F-1E1A-4055-8A2D-5129AE022ADB.png)
That Beta factor in the far right column is basically the rate at which scientists are ‘becoming less productive’ or rather, the rate at which scientists need to grow to maintain a constant growth rate in that area. Note that Moore’s law is actually doing pretty well. But nearly as well as Cotton, version 2! I wonder what’s going on there!

I (totally independently of the authors) have two ways of interpreting these betas. One way of looking at this chart might interpret high betas as dying or stagnant fields where not much improvement is possible. On the other hand, they might also suggest that because many weird and ambitious people have the former opinion, there might be opportunities for radically new paradigms that haven’t been uncovered. I (perhaps stupidly) like the latter.

The primary point of this paper is not actually making a strong claim about the vague sense many people have that we aren’t discovering or inventing as many amazing things as we used to. It is pushing back against the endogenous growth model of economics that assumes that a fixed number of scientists should be able to generate a constant rate of exponential growth. When put that way, my response is “yes? Was that a question?” Apparently it was. So it’s less “all you people are silly for expecting flying cars” and more “all you economists are silly for using unrealistic assumptions that make your math work.”

Hyperbole aside, the paper does have an important upshot: our null hypothesis should be that exponential progress is going to require exponential inputs and specifically exponential numbers of researchers. [Scott Alexander does an excellent job arguing that logically, this should be the case.](https://slatestarcodex.com/2018/11/26/is-science-slowing-down-2/) 

All things held equal, that paints a picture where we first train the entire human population to be researchers to maintain growth and then because human population is not growing at an exponential rate, growth stagnates anyway. That’s a bleak vision. In the short term, it underscores the importance of enhancing research productivity any way we can. I would argue that many trends have been moving the opposite direction. But, as Scott notes, it’s a bit absurd to think that we could make researchers exponentially better through better institutions alone. The beacon of hope, as he notes, is to decouple exponentially increasing research inputs from needing exponentially increasing researchers. The analogy I like is to nitrogen-based fertilizers. Before [the Haber—Bosch process](https://en.wikipedia.org/wiki/Haber_process)), nitrogen-based fertilizer (and thus crop output) was coupled to the supply of organic sources of nitrogen - especially bat guano. Supporting an exponentially growing population required an exponentially growing food supply and thus an exponentially growing supply of guano. [People literally fought wars and killed each other over bat poop.](https://en.wikipedia.org/wiki/Chincha_Islands_War) Given everything that he knew, Malthus was absolutely right - the way everything was going, the world was going to run out of guano, and thus fertilizer, and thus food, resulting in mass starvation. The Haber—Bosch process pulled nitrogen out of the effectively infinite atmosphere, decoupling nitrogen from organic sources. We didn’t stop needing exponentially more nitrogen, it just no longer required exponentially more guano. 

Can we replicate the Haber—Bosch process for research? I don’t know. Scott suggests that AI might be be the equivalent of the Haber—Bosch for research. I hope he’s right, and if he is, it suggests that it’s important to work on automated research that doesn’t require full general AI. [^1]

At the same time, it’s also worth asking what the implications of those different beta values are. The paper is light on *absolute* numbers of researchers. It’s a very different scenario if we would need the entire world’s population to do research within a generation vs. hundreds of years. A quick Google search suggests that there are about 8 million researchers in the entire world. According to the data in the paper, the number of researchers needs to double every 17 years to maintain constant growth rates. That means that in 2037 we would need 16 million researchers, 2054 we’d need 32 million and 2071 we’d need 64 million. Generously assuming a constant population, that’s 1% of the world’s population in 2071 doing research to maintain 20th century growth rates. Assuming the doubling rate of growth-maintaining researchers doesn’t *accelerate* (which unfortunately seems possible) those numbers suggests that while we need to break the researcher-idea production coupling we’re not in a doomsday scenario. At the same time, if you buy into the importance of science-driven growth [^2], it is clear that we can’t continue doing things exactly as we have in the past.

#### If you’re interested in this topic, some other things worth reading 
- Scott Alexander’s [Is Science Slowing Down?](https://slatestarcodex.com/2018/11/26/is-science-slowing-down-2/)
- Patrick Collison and Michael Nielsen’s [Science is Getting Less Bang for its Buck](https://www.theatlantic.com/science/archive/2018/11/diminishing-returns-science/575665/)
-  Jay Bhattacharya and Mikko Packalen’s [Stagnation and Scientific Incentives](https://www.nber.org/papers/w26752)
- Tyler Cowen and Ben Southwood’s [Is the rate of scientific progress slowing down?](https://www.brown.edu/academics/political-theory-project/sites/brown.edu.academics.political-theory-project/files/uploads/Innovation%20%26%20scientific%20progress.pdf)
- Jason Crawford’s[Teasing Apart S-Curves](https://rootsofprogress.org/teasing-apart-the-s-curves)
(Very open to other suggestions to add to this list!) 

[^1]: There has been some cool work on this subject, but it feels underdeveloped compared to the brunt of AI research. 
[^2]: Full disclosure: I do think science-driven growth is important. My reasoning follows the lines of Tyler Cowen’s arguments in [Stubborn Attachments](https://www.amazon.com/Stubborn-Attachments-Prosperous-Responsible-Individuals/dp/1732265135/ref=sr_1_2?dchild=1&keywords=tyler+cowen&qid=1595524269&sr=8-2) and [Peter Thiel’s arguments around the relationship between growth and violence](https://medium.com/@arnoldkling/peter-thiel-and-eric-weinstein-annotated-bd1de574d4bd#:~:text=At%20an%20hour%20and%2032,among%20humans%20is%20very%20high.).



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# Of Flying Cars and the Declining Rate of Profit
- Marxist argument that mechanization led to declining profit
- Argues that slow down actually started in 50’s
- Atom Bomb:Germany::Space Race:Russia
-
![](BearImages/3D797FC0-A114-4352-B610-1FB201DCB254-8241-000A00C747EECFA0/089C093B-7FBA-4C20-A7D8-F3ECBAD95EA2.png)
![](BearImages/2CD74A0A-6FFE-435E-9FED-275CADF9C5B6-8241-000A00CE4A1D1ECF/A284DF7A-E3E7-440C-BEF5-CC2EBDCBEEBB.png)
![](BearImages/E4119C69-5042-4E11-84B9-25437C61635E-8241-000A00D2C8918336/9654397C-70B0-4721-8676-EDC05A058633.png)
![](BearImages/FCDBA9F2-6C79-42FF-AF6D-AC9399F8F78D-8241-000A00D8A10662F2/10B09DB7-1E11-4380-A15A-60544CD0178C.png)
![](BearImages/628FFE90-795F-413B-9BF6-5AFCAC1FEC25-8241-000A01F24B900D7D/00819414-2C12-4D02-A5A7-48CF3A78307A.png)
![](BearImages/70890356-19AA-4E9C-96A0-44CD8B101AAA-8241-000A01F6750FB34A/99BC7D31-39E1-4EDF-BAAE-37C2FC5130BC.png)
![](BearImages/000C0B14-F00D-4536-8D56-C3195782AA7E-8241-000A01FEFCB69600/E560EF7E-0E2F-4568-9860-70D39EE62328.png)
::Is this true?::
![](BearImages/B8A62187-4CF2-44FF-A870-7416BADD0971-8241-000A020738E58943/15E24E82-D9FF-4712-BCB7-25394CA20D3A.png)
Errr….
![](BearImages/A542CF0A-A23B-4A92-B5C7-957856DD8D69-8241-000A020DEB275D88/8ABA63C9-0D88-4109-AD5F-0B7200FB3DF9.png)
Capitalism did lead to the advances up to mid 20th century
![](BearImages/063E5B78-7C42-41F0-8FF4-4468EDF2B98A-8241-000A0225E352C145/755E746C-A9E8-4E65-96BC-B1D259431EBD.png)
![](BearImages/1DB958B3-9F9B-451B-81BD-132794F618F4-8241-000A022D32185592/D1FF124E-0882-4A33-B082-AD875E57FFAB.png)
![](BearImages/265F4A9E-22CE-4DEA-A893-D53F18AFF40D-8241-000A023470DB05B6/29E8D39C-3023-48D1-9DC9-4422E8FAC749.png)
![](BearImages/DE1495EC-F877-4DEE-BA54-1C55DA840E00-8241-000A02393C6C8D67/151C7D78-F1BD-4840-A83B-51FBF16B30C5.png)
- Technologies and drugs that emerged are perfect for surveillance and control 
- 
![](BearImages/CCF123EC-0B0F-4039-9975-608A763E6CB1-8241-000A02550030AF98/DFA77679-C6DB-432A-B430-6F88B560D542.png)
![](BearImages/B7BF467F-056F-4693-A213-00703E266E53-8241-000A025D0F4A36D1/6BEE0C14-A863-410B-BBA3-645B85CE028B.png)
- Agrees that research funding has increased
- Argues that a lot of research is locked up in “Big Science” like the human genome project
- 
![](BearImages/A54B3C91-B1CB-4278-83D1-531BB1F66EF6-8241-000A026AB902FC09/23025FED-A78D-4CEA-9A83-2618E6BD1A1F.png)
![](BearImages/C166A563-873C-4EB0-A55A-217DF6CBEAEC-8241-000A026E1606580B/7D89F42A-00DA-4EDA-87DC-27DFB70EC7BD.png)
![](BearImages/A820C3A4-39AB-4293-A2B0-21B2E8317007-8241-000A0273D9ABEB82/61D2C9FE-6F2C-4703-8DD9-989769E2E9C1.png)
![](BearImages/2A1A9861-3015-4741-9805-0776B84E2CB4-8241-000A02776D958E53/CEACAB2A-3D8B-48B6-B013-CC582631A560.png)
![](BearImages/BE4B661F-294E-4B8B-A622-3812AF02E2F2-8241-000A0279BFD65266/CC34C448-E2DB-469C-8F6C-98932FE76212.png)
![](BearImages/6B49F10C-8B51-4F40-8D48-1E6BBFF72521-8241-000A02D0BB8053C5/8F43F905-CBD3-4189-B0BB-1380583A943C.png)
This seems to be a consensus and yet we don’t do it
![](BearImages/2B6CC17C-D204-47D5-A938-9BAFDD9F52A8-8241-000A02DC09A64828/4303E19D-60D8-456B-AA73-A49067869D34.png)
![](BearImages/5653E7F6-A345-4940-802B-F4F5E1D3B90A-8241-000A02E4E4164502/51F1AB1B-56D2-47D8-BA4B-1F82485149E0.png)
![](BearImages/4D496719-6E9B-49DF-88D4-47DB3F814754-8241-000A02E9593E5A99/DBD0F405-0E78-4F49-9EBD-E20DC23927FF.png)
![](BearImages/F2A9547D-6080-402C-B9CA-49FFB6A5431D-8241-000A02ED9A39F7F7/AFAAC91F-790C-4509-B29F-03ED061C3599.png)
![](BearImages/94ED3F4E-1A83-4EE1-AF38-6F1077A5CD3F-8241-000A02F403CB13F2/BB43E044-C47C-49C1-B26A-67CE2164A051.png)
- Poetic vs bureaucratic technologies
![](BearImages/3D53E5EB-06B2-477C-B99E-EA03CF243E07-8241-000A0307063D5F15/803A945A-673E-42D3-80F8-7A740AC4540B.png)
![](BearImages/9FEA6980-78B4-416D-9A0C-05FE85282CC1-8241-000A0309229B9BFC/D669B5BB-D1D2-4E8E-AC55-764E4DBE7AE8.png)
![](BearImages/F1D4AC07-09B5-4D7C-960D-173759DFE904-8241-000A030D3B5CAD45/FA85E791-D1D1-4D4E-B759-6F01FBD69274.png)
![](BearImages/7B5534D1-C1FE-4665-B349-5C65DB0F70D4-8241-000A03129A74A2DA/83E8DAC8-C9E0-4B37-A06E-3B3EA993A7C4.png)

### Published
2012
## Questions
## Thoughts
## Related
- [[Stagnation]]
It’s like the marxist version of [[Where’s my Flying Car - A memoir of futures past]]
## Authors
## Link
[Of Flying Cars and the Declining Rate of Profit](https://thebaffler.com/salvos/of-flying-cars-and-the-declining-rate-of-profit)



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The ability to say ‘huh that’s funny’ is important for discovering new phenomena

The Knowledge Machine

Quadrant Model of Scientific Research

Materials and manufacturing underpin civilization

Someone needs to buy manufactured technology eventually in a monetized economy

You need to break rules to create new paradigms

Theory can suggest where to dig for new phenomena

Scientific inquiry is an abstracting process while engineering design is a narrowing process

Both Bundling and Unbundling parts of a process is important

Phenomena-based cycles are stuck

It takes roughly 20 years to exploit a phenomenon at scale

TRLs are not monotonic

Contemporary research has multiple failure modes

Multidisciplinary teams need to leverage different paradigmatic lenses

Bell Labs and AT&T had timescale alignment

New phenomena can be exploited without understanding or understood without exploiting

A history of matter

A theory that is not fully correct can still be effective

Science and knowledge of the universe as raw resources

The term ‘Double edged sword’ is bothersome

# Phenomena-based cycles 
If you think of basic building blocks as 
1. Theorizing around  [[phenomena]] (why it happens) 
2. Observing phenomena (seeing it happens)
3. Exploiting phenomena (using it to do something useful) 
([[Phenomena are things that happen without a human consciously making them happen]]) 

There are actually several ways that things happen. 

**A couple of notes**
- The end of one “chain” is almost *always* the beginning of another, so a big picture would show a loop back to the original block. 
- For a given phenomena, there are often multiple chains running in parallel: throughout history people have observed already-theorized phenomena without any knowledge of the theory. The discovery of the cosmic microwave background is a perfect example of this. 
- “Exploiting” is itself a multistep process with its own loops. The [[Technology readiness level - TRL]] system gets at this fact and still ignores the dispersion aspect of technology that might be necessary before it can be used sufficiently to lead to an observation or theory. However, an observation or theory can *also* come from an incomplete exploitation. 

1. **Theory -> Observation -> Exploitation**
This is the [[Karl Popper]]Ian cycle that is also encoded in the idea of basic-applied research. It is the broad outline of how the [[Manhattan project]] worked - theory predicted that a critical mass of fissile material should explode, informing experiments where that was observed, leading to the exploitation of that phenomenon by building the bomb. The explosive, era-defining example of this mode embedded it in many minds. 
2. **Observation -> Exploitation -> Theory**
This is arguably how innovation has happened throughout most of history. “Hey, when I mix this special rock with this other special rock and water it turns into concrete!” Yes, that’s because those rocks have just the right mix of calcium, silicon, aluminum, iron, and other ingredients. “Huh, when I stir wheat-water with this special stick, it turns into beer!” Yes, that’s because that stick is maintaining a colony of yeast - tiny microorganisms that turn the sugars from the wheat into alcohol.
3. **Exploitation -> Observation -> Theory**
This is the “technology-based tool building” mode of phenomena discovery. Scanning electron microscopes or X-ray crystallography are great examples of this. Both take advantage of new phenomena to observe previously unobservable phenomena that can then be theorized around. Note how many Nobel prizes have been awarded for creating new observation tools. 
4. **Theory -> Observation -> Theory**
This is the [[Thomas Kuhn]]sian cycle from [[Cycles of Invention and Discovery]]: someone observes something that doesn’t fit with a previous theory which then forces an update to the theory. Galileo is the classic example here. Note that arguably Galileo is *also* an example of a type #3 chain because he was able to make the observation because of the new ability to exploit the lensing phenomena of polished and shaped glass. It’s rare that someone goes into an observation completely theory-free.  
5. **Exploitation -> Observation -> Exploitation**
This is basically just [[Learning by doing]]. Like #2 this is probably one of the most common modes and often operates a bunch of little loops parallel to #2 until theory comes along. It’s super common even in the 20th/21st century - the early days of rocketry were mostly trial and error. 
6. **Exploitation -> Exploitation**
Often, a new way to exploit a phenomenon becomes unlocked just by crossing some ability threshold. For example the ability to do deep learning was unlocked mostly by data and compute reaching a threshold - all of which are arguably exploiting the semiconductor phenomena. *However* because phenomena and thus paradigms are fractal ([[Phenomena are things that happen without a human consciously making them happen]]) this mode can look like a capability advance within a paradigm enabling another capability advance within that paradigm or a capability advance in one paradigm enabling a capability advance in another paradigm.
7. **Theory -> Theory**
Theory can also be built off of the back of other theories. This chain is a two-sided sword. On the one hand, it can enable great logical leaps to escape local equilibria. On the other hand, it can lead to entire fields becoming unmoored from reality. I will not name them here. [[Decoupling from market discipline is like cave diving]]. 

I didn’t realize until after writing this that theory doesn’t ever seem to lead directly to exploitation. The closest might actually be in computer science - especially cryptography where there is a very small gap between theory and reality. 

These cycles are how different axes of research affect each other. [[Research has many orthogonal and non orthogonal classification axes]]

Through this lens, I worry that thanks to specialization-driving forces in the 21st century ([[Competition and lack of slack  creates specialization]]) we are putting more and more effort into chain #6 and #7, without actually completing any loops. Uncompleted loops could absolutely lead to [[Stagnation]].  If this narrative is the case, we need to start asking ‘how can these phenomena we’re exploiting lead to better observations or theories about *different* phenomena?’ [[Phenomena-based cycles are stuck]]

### Related
- [[The ability to say ‘huh that’s funny’ is important for discovering new phenomena]]
- [[X-punk imagines a world where people didn't harness any new phenomena beyond X]]
- [[Technological and scientific paradigms are each based around exploiting a specific phenomenon]]
- [[Heuretic domains share an underlying phenomenon]]

### References
- [[Cycles of Invention and Discovery]]



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# Progress getting exponentially harder should be the null hypothesis
Argument made by [[Is Science Slowing Down? - SSC]]

### Related 
- [[Stagnation]]


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Lowering barriers to entry is clutch for healthy institutions

We have no flying cars - failures of paradigm-shifting engineering

Individual rights conflict with societal progress

Once you create a regulation you don't even have to argue anymore

Classical Liberalism defaults towards gridlock

‘Yeah but it’s dangerous’ isn’t a valid argument not to do things

Piecewise intervention doesn't work well for complex systems

Metrics can cause paradigm lock-in

Systems engineering was only invented in the 1950s

# Regulations create paradigm lock-in
Regulations effectively lock in a paradigm.  This paradigm lock in can run a spectrum from more explicit to less explicit. The explicit end looks like mandates about specific technology and design choices:“your car must have a catalytic converter.” The less explicit end looks like certain measurements that need to be hit “you can only emit X amount of CO2.” Even measured quantities lock-in a certain paradigm about what is important to measure. From [[Thomas Kuhn]], paradigms `provide model problems and solutions for a community of practitioners, i.e., what is to be observed and scrutinized`.  [[Paradigms outline a set of questions and the rules around answering them]].

A lot of these paradigm lock-ins happen because [[Many things cannot be measured well]] - often we can’t measure safety directly so you need to use proxy measurements that assume a certain paradigm of “what’s safe.” These proxy measurements is why cars are required to have a certain sized crumple zone and thus all have the same shape. It’s much easier to measure physical characteristics of a car than to do a thousand crash tests and see what percentage of them would kill the dummy. 

Regulations also lock-in priority paradigms. The supersonic flight ban locks in a paradigm that prioritizes volume levels near airports over transportation speed. Built into that are implicit assumptions about how loud sonic booms are and a utilitarian calculus about the number (and sort of) people who would be harmed by the noise pollution vs the people who would benefit from having supersonic flight. 

If you take a politics-neutral definition of conservatism as being averse to change, once in place regulations are inherently conservative. (Unless you are conservatively trying to prevent the change that is the regulation itself.) 

[[Regulations slow down technological advancement]] follows naturally through the lens of paradigm lock-in.

### Related
- [[Once you create a regulation you don't even have to argue anymore]]
- [[Changing paradigms is hard]]
- [[Paradigms are lenses for looking at the world]]
- [[Where’s my Flying Car - A memoir of futures past]]
- [[Labs as regulation-free environment]]
- [[Stagnation]]



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Luke Constable Podcast

# Shareholder revolution caused public companies to focus on short-term profits
https://twitter.com/bkavoussi/status/1302294824138268672?s=12

[[Stagnation]]



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Phenomena-based cycles

Graduate students are the labor in academic science and engineering

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

Competition and lack of slack  creates specialization

Exponential hardware does not lead to exponential software

Disambiguating stagnation arguments

Progress getting exponentially harder should be the null hypothesis

Regulations have increased by an order of magnitude

Technology has enabled progress to be a net positive

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

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

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

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

Is Science Hitting a Wall?

Of Flying Cars and the Declining Rate of Profit

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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One Process - Post

§Tech Tree Model of Heuretics

Academia incentivizes novelty, not focus

Einstein would have been stuck in a patent office - failures of paradigm-shifting science

Ars Longa Vita Brevis - SSC

Lee Ricketson conversation 6/5/20

Stagnation, scientific incentives, and funding models - Roots of Progress

Heuretics cannot exist separate from humans\*

Notes on the Endless Frontier Act

# Stagnation and Scientific Incentives
### On Low Hanging Fruit
[[Low Hanging fruit theory of stagnation]] is `convenient as it absolves scientists, institutions, and analysts from any responsibility for the slowdown, it is likely wrong. One problem is that this explanation is not unique to our era. `

### Core Hypothesis
`In this paper, we argue that the root of the slowdown in scientific progress lies in the more quotidian fact that scientific work on truly novel ideas that have the potential to develop into groundbreaking advances is no longer rewarded in the same way it once was. `

### Theories for problem
`if also novelty metrics were utilized in scientist evaluation, scientists might pursue more innovative, riskier, projects.`
Changes in scientific incentives→ slowing science innovation→ economy
Citations shifted behavior towards incremental science
- Science requires exploration and play
- Lifecycle of scientific ideas look like S curves
	- But the height of the S curve is variable, and it’s hard to know what its parameters are
- Citations make scientists more conservative
	- Eugene Garfield developed the idea of citations as a measure of science productivity
		- ::How did it work before?::
	- `The citation revolution started to gather steam in the 1970s.`
		- ::[[WTF happened in 1971? - Article]]::
	- - `In recent decades the largest scientific funding agency, the National Institutes of Health (NIH), has also become less likely to support novel work despite its best efforts to the contrary.`
![](BearImages/DE686EC7-350A-4C31-BBA9-ADB71B8D9B25-68756-0005E4EDFA984E25/AF030A33-7420-4241-ABFB-F2DEAD21CB5A.png)

- Academic Search Engines solidify citation obsession
	- Only see citations, not what sort of exploration it was
	- ‘winners win harder’
	- ::The internet helps the tail *if search is good* and in this case it’s not::
	- Just counting papers is a possibility that wouldn’t help either
	- Science administrators are the ones focusing on the citations
	- Possible solution is to measure and present exploration level
	- Baseball players are evaluated on more dimensions than scientists: see [[Sabermetrics]]
- Measureing exploration in science
	- Pushback that scientific stagnation is inevitable 
	- `Of course, to reward scientific novelty, we must first measure it` 😭
		- Suggest a textual analysis of research publications
			- [[Undiscovered public knowledge discovery overindexes on things that have good databases]]
			- [[Most human knowledge is not encoded]]
	- Idea of Edge factor + Impact factor
`The third possible path forward could put science back on the path envisioned by Vannevar Bush by broadening how scientific productivity is measured and evaluated. On this path, in addition to scientific impact, we start measuring and evaluating research based on what kind of science it represents—whether the work is novel in that it tries out new ideas or more conventional in that it seeks to advance well-established ideas.`

### Published
2/2020
## Questions
- Which science friends care about citation counts - in some fields it’s much more about who knows you. Might be similar to startups in that if the right people you know you but there’s a dominant game otherwise.
- Could citation counts be like customers?
## Thoughts
- Funded by a grant from [[National Institute on Aging]]
- The administration of science by people who don’t get the science is a problem
- [[Stagnation and Scientific Incentives argues against the low hanging fruit hypothesis by historical analogy]]
## Related
- [[Technological S curves]]
- [[How do you identify where a technology is on an S-Curve?]]
- [[There is an increasingly wide gap between the number of graduate students training for PhDs and academic positions for them to fill]]

#### Cites
- [[Are Ideas Getting Harder to Find?]]
- [[Is the Force Awakening?]]
- [[Science is getting less Bang for its Buck]]
- [[Is the rate of scientific progress slowing down?]]
- [[Quantifying the Ease of Scientometric Discovery]]

#### Non-cited refs
- [[Is Science Slowing Down? - SSC]]

## Authors
- [[Jay Bhattacharya]]
- [[Mikko Packalen]]
## Link
https://www.nber.org/papers/w26752

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# Stagnation and Scientific Incentives argues against the low hanging fruit hypothesis by historical analogy
[[Stagnation and Scientific Incentives]] addresses the low hanging fruit analogy ([[Low Hanging fruit theory of stagnation]]) by analogy to previous feelings of stagnation

`“The more important fundamental laws and facts of physical science have all been discovered, and these are now so firmly established that the possibility of their ever being supplanted in consequence of new discoveries is exceedingly remote.”` — Michaelson of michaelson and morely 

Is this actually true though? General relatively is super cool but is it more consequential than Maxwell’s Laws? Germ Theory? What during the 20th century was more consequential than in the 19th? Or is that a dumb question?



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Fair games are self-limiting

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

# Successful professors are not necessarily the best researchers
[[Professors do three or more jobs]] but at the end of the day they are directly judged on their lab’s research output and both directly and directly on how much grant money they bring in. The professor’s lab’s research depends not just on the professor’s ability to do research but (arguably more) on their ability to manage, attract good grad students ([[Graduate students are the labor in academic science and engineering]]), and bring in enough money to keep the lab well funded. The professor’s name and reputation goes on everything that comes out of the lab, so there is incentive to have the biggest lab you can without compromising quality (and possibly there’s even a Pareto-front where you compromise quality a little to 

Therefore, the most successful professors are usually the ones who are the best grant writers and managers. A professor’s success is not entirely decoupled from their ability to do research because [[People who have done a thing should be in charge of a thing]]. 

The more a field depends on large labs and expensive equipment, the less professors do actual research because they need to recruit and fund graduate students. [[Science has become more capital and labor intensive over time]]

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



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# 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]].

[[Where’s my Flying Car - A memoir of futures past]] 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 ([[Science is getting less Bang for its Buck]]) 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. [[Where’s my Flying Car - A memoir of futures past]] 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. [[The changing structure of American Innovation - some cautionary remarks for economic growth]] 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.  [[Radical Abundance]]  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. 

### 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]]

### Works not cited in the body
- [[Killing the Golden Goose? The changing nature of corporate research, 1980-2007]]

[^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. [[Studies on Slack - SSC]].



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Classical liberalism arose in an environment that no longer exists

Archipelago and Atomic Commutarianism

# Technology has enabled progress to be a net positive
Today, people generally agree that on net, change is good. The sticking point is about *which* changes are good and which are not. New career opportunities pop up, self improvement, we can meet so many new people, social mobility, and of course, continuously improving technology. 

In the 16th century, this was absolutely not the case. For most people and for most part, change came in the form of someone conquering the land you lived on, burning down your village, or increasing the tax rate. Change as a mostly negative thing had only just started to change by the 18th century when Classical Liberalism was encoded.  [[Classical liberalism arose in an environment that no longer exists]].  

The story is told to death - the first industrial revolution kicked off in the mid 18th century and we were off to the races. It actually doesn’t matter whether the industrial revolution was simply part of an exponential trend or a true discrete change. What matters is the people *perceive* it as a step change - in the same way that  [[Human intuition treats all distributions as lognormal]], we mistake continuous exponential growth for discrete changes. 

Now, change and progress have default positive connotations because things keep getting better. Technology enabled people to starve less, die of less disease, communicate better, learn more, spend less on lighting, etc. etc.  Things still change for the worse sometimes - wars break out, MacBook keyboards fail, etc. At the same time, many things have gotten better because of not-technology: ending slavery, universal suffrage, etc. Some people would argue that these policy changes had more positive impact than the technological changes. It’s a complicated issue to argue, but I would point out that the policy changes may have been in part *enabled* by the increasing wealth generated by technology and that for most people, technology has had a bigger impact on day-to-day quality of life. Of course, the counterargument to the day-to-day quality of life argument is that being the most comfortable, pampered slave in the world is still slavery. I actually find that argument compelling. So perhaps we should think about median change instead of mean change. That is, of all the positive changes, most of them (by number) have been thanks to technology. For at least the past century change (hence PROgress) has been net positive thanks mostly to technology. 

This idea is probably explored more fully in [[A culture of Growth]] and it is, to my reading, [[Jason Crawford]]’s primary thesis. 

This idea is also why [[Stagnation]] should be concerning. [[People are very sensitive to what works]], so if technology fails to deliver positive progress it can create a cascading feedback loop. 

### Related
- [[Classical Liberalism defaults towards gridlock]]
- [[§Type I and Type II Progress]]
- [[Post-liberal synthesis]]
- [[People who are directly affected by the change often impede progress]]
- [[Consistent areas that matter for human progress]]
- [[Maslow’s hierarchy is a leaky abstraction]]



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Peer review has failed as an institution

Peer review doesn’t catch duplications

The way to get around journals is to focus on non-paper artifacts

People do fundable work

Academics are incentivized to do peer-reviewed publishable work

Maverick work is lonely and DARPA provides peers and community

DARPA provides a derisking role for people in other organizations

The systemization of science since the 1950s treats all fields the same

Peer review gives peers veto power over each other

A system of credit and funding for non-Scientific Paper artifacts should be artifact-type agnostic

Using citations to measure science is insidious

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

# The peer review and citation system incentivizes people to work on things that other people think is interesting
You could argue that this is a good mechanism that forces people to work on relevant work. On the other hand it enforces memesis. ([[Memetic Theory explains a lot of human nature]])

Put in a more extreme way [[Peer review gives peers veto power over each other]]. If something is too heretical, it just won’t make it through peer review. 

Even if there is funding available to do crazy shit you wouldn’t do it if you couldn’t get the results published and cited because [[Academia is the game where you gain status by getting attention for new knowledge]].

![](BearImages/D70F62F9-2CA8-4B86-851D-505094A86D26-61320-000774DAF820EE3A/FD1B16C6-91A1-44CF-98EC-44CDE6D5AD96.png)

As a [[Stagnation]] theory this could be attributed t 

### Related
- [[Peer review doesn’t catch duplications]]
- [[Peer review has failed as an institution]]
- [[True Peer Peer Review]]
- [[People do fundable work]]

### References
- [[Peer Review- Rodney Brooks]]
- [[Is peer review a good idea - paper]]
- [[Problems With Peer Review And Alternatives]]
- [[Three myths about scientific peer review]]



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You can create a program design discipline that enables better research and development

Week of 14 Dec 2020

# The possible upsides of a healthy program design discipline seem like they could be huge
While it wouldn’t solve all the [[Stagnation]] problems, arguably a healthy program design discipline could address several symptoms. 

**Program design could potentially help programs exist that would not exist otherwise.**  I suspect that many programs never get off the ground because of a vicious cycle in which nobody is willing to fund them because the risks and possibilities are so incredibly vague and its unclear how small amounts of funding could clarify things. A program design discipline could help crack this chicken-egg problem. 

**Program design could potentially prevent wild goose chases.** While [[Efficiency is overrated]], especially in the context of research, better program design could help prevent the situations we see over and over again where a large amount of money is thrown at an area with little effect because it’s not actually going towards key bottlenecks. A discipline of program design could help people wrap their heads around those bottlenecks and not only direct resources towards them but encourage out-of-the-box solutions to them. It’s much easier to come up with innovative solutions to a well-designed problem. “Interdisciplinary work” is a [[Suitcase Handle Word]] that people often take to mean “let’s get a bunch of random interested people in a room.”

**Program design could potentially help uncover unintuitive possibilities**. A powerful reason to systematize a discipline is when it can lead to unintuitive (and correct) conclusions. My favorite example of this is how mechanics and geometry suggests the way for two people on either end of a rotating plank to toss a ball back and forth is to throw it *behind* themselves instead of where it looks like the other person is going. [[Architecting Discovery - Article]] suggests that similar intuitive leaps might be possible through a systematic discipline. I tend to agree with this hope. 

Systematically uncovering unintuitive possibilities is one of [[Undiscovered public knowledge discovery - UPKD]]’s core aims. There are strong overlaps between the two disciplines and I suspect that program design should use many of the tools in the UPKD toolbox. There are two big differences I can see. First, program design focuses on figuring out what actions to take *in order* to create new knowledge, instead of trying to create that knowledge directly. Second, UPKD tends to treat knowledge as a thing that exists separate from the people who created it while humans are a core piece of program design. 



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Nobody grades moonshots on effort

# The term moonshot is romantic bullshit
When you’re not literally going to the Moon, the term “Moonshot” is a [[Suitcase Handle Word]].  People try to impose different technical meanings to it (“it involves a lot of engineering”, “it needs to have knock on effects for economies”, “it needs to be inspiring”) but it doesn’t actually mean anything besides “ambitious project.” 

![](BearImages/665E1960-CF96-40A7-B184-761640781326-88049-00040592C4986DAF/5E58D994-A434-47BD-AD43-6A6B14574682.png)
There are two reasons the heavy use of the word “moonshot” is bad. 

First, moonshot inflation is real. The term has become diluted to the point that it really had no specific meaning besides “I think this project is ambitious and I want you to get as excited about it as you got about the space race.” However, when so many people are using it in so many different ways about so many projects that aren’t particularly exciting, ambitious, or successful, it just makes everybody so jaded that we can’t evaluate what to get excited about. 

 Second, and more subtly, it encourages us to look to the past instead of looking forward. When we call everything a moonshot, we implicitly look to the past instead of the future. In a way it’s setting up a yardstick that turns even space exploration into a relative game. [[Relative vs absolute games]].

### Related
- [[Bullshit Ideas]]
- [[Stagnation]]
- [[Nobody grades moonshots on effort]]
- [[Google X]]
- [[Alphabet shuts power kites moonshot project]]



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# There are three main theories of stagnation
I’ve tried to form the theories to be as orthogonal as possible, but as much as we might like there to be fully orthogonal theories, we’re not going to get there.  However, it does seem useful to separate ideas about stagnation into useful buckets because each bucket suggests a slightly different class of intervention. And as a human I like categorizing things. 

1. [[Human capital theory of stagnation]]
2. [[Systemic decay theory of stagnation]]
3. [[Low Hanging fruit theory of stagnation]]



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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.… “]]
- [[Bret Weinstein - The Prediction and the DISC - Portal Podcast]]
	- Specifically the description of the grad school problem [[Graduate students are the labor in academic science and engineering]]



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Ben's working notes

Stagnation

List of as atomic as possible stagnation theories

Links to this note