Graduate students are the labor in academic science and engineering

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

Manufacturing is important and often ignored

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

All the incentives in research drive towards conservatism

Peer review doesn’t catch duplications

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

A new professor needs about $1.5m to get going

Academic culture prizes individual recognition

Younger professors have to spend more time getting money

Academics are incentivized to do peer-reviewed publishable work

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

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

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

Three myths about scientific peer review

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

Different axes of where research gets done in the US

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

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

NS Notes on Officers Techniques

Necrobotics illustrates how academia forces engineering

The boundaries between industry government and academia are fuzzy

Startups and Academia are bad at addressing structural constraints

The Architected Innov􏰂ation Institute - Engineering Scientific Revolutions

There are very few senior individual contributors in academia

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

Wolfgang Merkt conversation 12 Oct 2021

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

Academic administrators and professors are playing very different games

An Institute for Creative Technology - Riffing on ARPA

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

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

Running list of relevant science news?

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

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

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

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§Academia Constraints

Asymmetric career risk

The definition of the ARPA model has changed over time

Current value capture mechanisms are crude

How does money work in PARPA?

“The System” of AT&T

Problems that require system-level solutions often require massive teams

Top of mind

National labs and other government research centers are a research alternative to universities

Many innovations are not valuable on their own but can be combined to be valuable

Healthy R&D orgs feel like they have very smooth project rampups with high ceilings

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

We need new institutional structures to fill the role that corporate labs once occupied

Government research is corporate R&D for United States Inc

Profit-maximizing organizational structures can hamstring technologies whose impact depends on them

R+D labs require companies that can win based on Technology

Research programs that are constrained from existing

# §Corporate R&D Constraints
 Follow innovation through bell labs
	* Run into problem with “the system” - can’t fit enough calls into a single cable. More cables are expensive. Want people to make more, better, calls. Problems constantly coming down from the top and 
	* 
* Follow Innovation through GoFaAmSoft
* Politics 
	- [[Internal incentive structures within a large organization work against high quality R&D labs]]
	* [[R&D orgs are always on the side of an org chart - The lab is always separated from the mothership]]
	* [[Rewards in a big company operate on one timescale and R&D happens on another timescale]]
* The most valuable research for a company tends to be systems-level improvements. But [[Problems that require system-level solutions often require massive teams]] while [[Research tends to work best when it can be done by small teams]].
* Risk aversion
	* [[Asymmetric career risk]] 
	* [[Asymmetric career risk impacts corporate R&D]] 
* Incentives
	* [[The incentives inherent to corporate R&D mean that researchers motivated by money leave and those who are motivated by glory are not the best in the world]]
	* [[At the organizational level corporate R&D labs either cause brain drain or don’t have the best people]] 
* [[R&D orgs are expensive]]
* [[R&D orgs are answerable to shareholders both in scope and timescale]]
* [[Companies want R&D to be off of their balance sheets]]
* [[The siloization that tends to happen in large corporations hamstrings applicability and serendipity]]
* [[Big companies cannot start small things]] 
* [[R&D efforts need to be connected to a core product to maintain support]]
* [[There are several macro-economic analyses of both why and whether there has been a decline in corporate R&D]]
* [[Corporate R-D orgs experience a tension between Research and Development]]
* [[Corporate R&D orgs have a lot of momentum]]
* Trends
[[Corporate Innovation has four main options: partner, R&D, corporate venture capital, and venture building]]
<draw 2x2 box>
	* Externalize R&D through startups and universities 
	* Internal startups via Red Box at Swisscomm or Area120 at Google
	* “Innovation Arms”
	* Grants
## Related
- [[§Startup Constraints]]

                            

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Decoupling from market discipline is like cave diving

When should an idea that smells like research be a startup?

§Corporate R+D Constraints

Startups are good at point changes

Systemic decay theory of stagnation

Ideally corporate R+D enables work on a general purpose technology before it’s specialized

There are many tight feedback loops built into the ARPA model

One function of a private ARPA is to fill in the gap once filled by corporate R+D

There are many important innovations that are just bad investments

Startup Constraints - essay

The market in for-profit organizations is fairly efficient

PARPA could walk the line between profit and non profit

Startups  need to spend effort on sales, marketing, and productization

# §Startup Constraints
- [[Startups need to make a lot of money or have a lot of users]]

- [[Startups need to conform to investor timescales]]
- [[Venture capital doesn’t work for things that only realize their value on long timescales]]
- [[Startups need an exit strategy]]
- [[Startups need a great story]]
- [[Startups need to produce a scalable product]] 
- [[Startups need to break development into correctly sized stages]]
- [[Startups need specific growth curves]]
- [[If a project has massive possible upside and low enough risk it is venture fundable]]
- [[Scalability and venture fundability happens when you turn a process innovation into a tool innovation]]
- [[Startups are good at point changes]]

- [[Startup Constraints - essay]]

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gertnerIdeaFactoryBell2012

Bell Labs was only profitable in conjunction with AT+T

What are the different roles in an innovation org?

Optimizing individual components of a system is often at odds with the system itself

Bell Labs was not a business

Ideally corporate R+D enables targeted dicking around

AT+T benefitted significantly from creating better technology

One way for an R+D org to address existential threats is to tie into a core product

All the work at Bell Labs needed to tie back to the system

Why was Bell Labs Different?

# “The System” of AT&T
# “The System” of AT-T
AT&T thought of its interconnected communication network as “The System” - all pieces of the company could point back at “The System” and say how they were helping the system’s goals - which were around communication. 
Contrast this to  say Alphabet, where most of the products don’t actually point back to the same core.

## Related
- [[Innovation orgs need to be aligned with their money factory]]
- [[Innovation orgs need a money machine]]
- [[§Corporate R+D Constraints]]



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Why hasn’t Bell Labs been replicated?

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

Why try to emulate DARPA and not Bell Labs

Notes on the changing structure of American Innovation

ICT Distillation

26 Oct 2020

ICT Outline

Startups have taken over a chunk of corporate R+Ds niche

# AI research is an exception to the decline of corporate R+D
AI research bucks the trend of the decline of corporate labs as a place where globally important, cutting edge work is done. ([[The decline of science in corporate R&D]]) If you squint, Organizations like [[Facebook AI Research - FAIR]], [[Google Brain]], [[DeepMind]], and possibly [[OpenAI]] [^1] bear strong resemblance to [[Bell Labs]], [[Xerox PARC]], and other legendary Corporate R&D orgs (I’ll just refer to these as Legendary CR&D)  in their heydays.

The parallels manifest in several ways. It’s worth speculating on why they exist in these labs but not elsewhere to understand why the dynamics may or may not be replicable in more areas.

AI research (can) require massive resources - in this case it’s just thousands of dollars of compute for training models and the datasets to train on. These resource requirements mean that there is exploratory research that people just can’t do with the resources available to most labs at universities. You saw a similar effect pulling professors away from universities and into corporate labs in the first half of the 20th century. 

At the same time, corporations running AI labs reasonably expect the research to create value commiserate to the costs of this research. Machine learning can directly improve the core product lines of all the companies listed above, in the same way that Bell Labs’ work directly improved [[“The System” of AT&T]]. [[Innovation orgs need to be aligned with their money factory]]. Additionally, AI promises massive value over long but not infinite timescales. OpenAI’s business model is implicitly based on the assumption that they will create literally infinite value in the not unforeseeable future. 

This alignment between the research and the money factory allow modern AI labs to do highly regarded work and collaborate closely with academia, *without* seeming like a waste of money. Bell Labs was the home for the work that led to nine Nobel prizes. Today, AI conferences are dominated by work from corporate AI labs, not just in quantity but quality as well. 

Like the electronics work at Bell Labs, AI research benefits from having multiple disciplines in the same place and the people who will eventually produce it. When you’re creating “AI for X”, it’s generally helpful to have someone who is an expert in X around. Similarly [[Business and finance people are utilities]]. Training and implementing ML algorithms at scale requires a different skillset than creating and prototyping the algorithms. Corporate AI labs have the budget to hire research engineers and also make it easy for researchers to talk to some of the best infrastructure and production engineers in the world. This dynamic roughly parallels the close ties between Bell Labs and Western Electric.  [[Prototyping needs manufacturing in the room]]. 

The perhaps uncomfortable parallel is between AT&T’s status as a high-margin, cash-printing monopoly and Google/Facebook’s similar situation. AI labs suggest that successful corporate labs can only exist in domains where there are large corporations with monopoly-like power. The decline of corporate labs in chemistry and physics-related domains may have been caused by commoditization of those products. GE, Dupont, Kodak, and others’ share prices suggest that they at least are no longer perceived as monopolies. However, monopoly profits -> high quality corporate research isn’t the entire story because you don’t see a lot of high-quality non-product research coming out of Boeing and Amazon[^2] ::are there other examples::?  Perhaps the missing difference is whether or not the monopoly perceives that it will benefit from significantly better technology. [[AT+T benefitted significantly from creating better technology]] because they had an effective pact with the government at as long as they kept making the system better they wouldn’t be broken up. I wonder what the counterfactual is - being left as a monopoly without the pact. While Google and Facebook have no (publicly known) agreement with the government, I believe [[Peter Thiel]]? Made the argument that at least Google’s monopoly does depend on them continuing to have the best search engine - people have no loyalty to google beyond the quality of their searches. Similarly one could make an argument that advertisers will abandon Facebook as soon as they have worse ad targeting or the kids move to another platform like TikTok or Snap. 

Microsoft is an interesting beast to examine through this lens. It’s unconfirmed, but arguably [[Microsoft Research]] was started as a public offering in the same way Bell Labs was. However, it both explicitly set out to do more product-focused work than PARC and Microsoft’s cash cow was Windows and the Office Suite, both of which didn’t benefit much from MSR work. MSR was subsequently pretty neglected and used for stunts like China expansion. However, Microsoft’s core business has begun to shift towards cloud services, which *do* benefit from AI research and it’s becoming clear with things like GPT-3 that AI research can augment Office Suite products that are now actually under threat. This shift coincides with a[partnership with OpenAI](https://news.microsoft.com/2019/07/22/openai-forms-exclusive-computing-partnership-with-microsoft-to-build-new-azure-ai-supercomputing-technologies/#:~:text=The%20partnership%20covers%20the%20following,promise%20of%20artificial%20general%20intelligence) that looks a bit like the relationship between AT&T and Bell Labs if you squint. Amazon remains a challenge to this just-so story because it also has a massive cloud business but is not (publicly) doing as much speculative AI research.

If it’s accurate, this narrative suggests an uncomfortable truth. Instead of declining because of  “corporate short-termism,” speculative corporate R&D might depend on companies with monopoly-level margins perceiving their fate being intimately tied to a high-potential technology.  This trifecta (monopoly + clearly high potential technology + that technology being tied to the company’s core business) can’t be created by policy or even a cultural shift.  [[Healthy corporate R+D requires a trifecta of conditions]]. The trifecta is the most straightforward answer to the question [[Why hasn’t Bell Labs been replicated?]] and suggests that attempts to “create a new Bell Labs” are doomed to failure if they try to follow the playbook too closely. At the same time, I strongly suspect there are incredibly valuable technologies that don’t fit the criteria of the trifecta (clearly high potential to solve an existential threat for a monopoly rentee) but do need the environment and resources that were once provided by healthy CR&D labs to realize that value.  This is why[[We need new institutional structures to fill the role that corporate labs once occupied]].

### Related
- [[aroraChangingStructureAmerican2020]]
- [[Bell Labs didn’t have a unique model]]
- [[Corporate R+D orgs experience a tension between Research and Development]]
- [[§Corporate R+D Constraints]]
- [[gertnerIdeaFactoryBell2012]]
- [[aroraChangingNatureCorporate2015]]
- [[Does the nature of the activities to create new technology and science change over time?]]

[^1]: At this point OpenAI resembles the Bell Labs equivalent for Microsoft
[^2]: Maybe it’s a [[Seattle]] thing?



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Shadows of the Future

Changing paradigms is hard

DARPA Program managers pull control and risk away from both researchers and directors

Pieces of the Action Forward Intro Notes

Reaper functions beat culture decks

Human capital theory of stagnation

People are more averse to short term losses than they are attracted to long term gain

NSF Program officers can have multi-decade tenures

DARPA’s aversion to people with a web presence may be how they avoid asymmetric career risk

The only time you can get philanthropies to do interesting things is at the beginning

Lots of innovation is stifled because of the overhead of going off of the beaten path rather than direct forbiddance

People are hesitant to try things when they don’t know where the line is and the consequences are large

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

DARPA’s aversion to people with a web presence hints at a way that the internet has eroded institutions

How do you have an institution that avoids asymmetric career risk and going rogue in a world where everybody is a free agent?

Asymmetric career risk impacts corporate R+D

Often spending money is the primary goal over the output of that money

There are no perceptions of outsized returns to philanthropy

The problems we associate with government orgs is not caused by government per se but by low accountability vs high accountability institutions

Nadia Eghbal conversation 7 Jul 2021

Glossary

Career hysteresis

David Lang conversation 20 Apr 2021

# Asymmetric career risk
You can’t get fired for failing in the conventional way, especially early in your career. You can get fired or have you career hampered by failing in a weird way.

On the flip-side, in most career situations you don’t capture a lot of the value of the wild success that can come from taking weird risks. 
This is one of the [[§Corporate R&D Constraints]]

![](../BearImages/72D8815F-0DBD-4198-90E8-48026CE139D3-621-000434AF61C86F43/9E902B34-82EF-4CCE-BD2C-5FB18A6C9537.png)

### Related
- [[Shadows of the Future]]
- [[We do not give enough credit to good philanthropic funders]]



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

One of the most failure-prone points in technological development is when a technology needs to move from an organization that focuses on R&D to one that focuses on marketing and manufacturing

# Asymmetric career risk impacts corporate R&D
Someone always needs to go out of their way to make the output of a corporate R&D lab mainstream because  [[R&D orgs are always on the side of an org chart - The lab is always separated from the mothership]] and [[There is rarely a default path for R&D output to become mainstream]]. [[Asymmetric career risk]] is especially acute for this person because it is never ‘their job’ to push the technology through.  The solution to depending on asymmetric career risk for new things to ram their way into a company is to [[Manage the Transfer not the Technology]], but setting that up in the first place is going against the stream. It’s just easier to have [[Innovation Theater]].

In the late 2010’s companies tried to address this problem by creating “innovation teams.” From conversations with people at these arms - there is still no default bridge crosser.

If you look at the heralded labs of the 20th century like [[Lockheed Skunkworks]] and the [[Rad Lab]] many of them came within a hair of failure and only succeeded because of a champion. 

This is also a problem for startups trying to sell into large companies. 
[[Asymmetric career risk]] happens across all levels of an organization. CEOs are fired by boards for trying weird things as often as engineers are fired by managers for not putting their heads down and working on what they’re supposed to. 

### References
-  [[Conversation with Kelly 12/2]] 



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Corporate R+D orgs are expensive

Bell labs was primarily not researchers

Corporate R+D orgs experience a tension between Research and Development

Notes that get screwed up somewhere in the pipeline

#  At the organizational level corporate R&D labs either cause brain drain or don’t have the best people
#  At the organizational level corporate R&D labs either cause brain drain or don’t have the best people
# At the organizational level corporate R-D labs either cause brain drain or don’t have the best people

If you want the best people in your R&D org, you need to make working there the most prestigious role in your org. If you make the R&D org the most prestigious part of the company, then everybody will want to work there. [[Organizations always have a most prestigious role]].  If everybody wants to work in the R&D org, that will cause brain drain on the rest of the org and the status differential will generate resentment from the people who are not part of the lab because you’re implicitly telling them that they’re not good enough. [^2]
Conversely, if you *don’t* make the R&D org the most prestigious part, you won’t get the best people. Not having the best people will hamstring the R&D org since [[Power laws in people quality matter more in high-variance activities]].  
[^2]: Looking at the list of examples of prestigious roles suggests some ways to mitigate it. You could make getting into the org so obscenely selective that even good people can’t get in (special forces, astronauts.) 
Much of this is from [[Conversation with Charlie Songhurst 12/2018]]



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Innovation orgs need to be aligned with their money factory

Bell Labs

Alignment requires existential threats

The incentives inherent to corporate R&D mean that researchers motivated by money leave and those who are motivated by glory are not the best in the world

§Bell Labs Model

Labs Document Outline

# Bell Labs is one of the few examples of an innovation org that was aligned with its money factory
 [[Innovation orgs need to be aligned with their money factory]] and arguably one of the reasons behind [[Bell Labs]]’s outlier success was that it managed to have a broad scope and long time scale that were both *still aligned* with AT&T.

At the end of the day AT&T’s goal was to maintain its monopoly and increase its business. In order to do that, it needed to enable people to make more, better calls. 

[[“The System” of AT&T]]  had such an expansive scope that it actually benefitted from the whole spectrum of activity happening at Bell Labs. Everything from information theory to novel materials directly fed back into AT&Ts core business. More cables are expensive so information theory actually helps people make more calls. The faster you can switch, the more calls you can route so semiconductor switchers *directly* help with the goals. 

Additionally, [[Bell Labs and AT&T had timescale alignment]] thanks at least in part to AT&T’s monopoly. The monopoly enabled Bell Labs to plan on multi-decade timescales because they didn’t have to worry about a competitor coming along tomorrow. 

AT&T maintained their monopoly only with tacit approval from the government. This approval rested heavily on Bell Labs’ output - both continuous improvements to A&+Ts offerings and open licenses from the lab. [[Regulation drove many of Bell Labs’ actions]]. 

Between the cost reductions and monopoly maintenance, Bell Labs was actually addressing existential risks to AT&T. [[Alignment requires existential threats]]. Even R&D labs that are building new product lines within the scope of a company’s actives rarely address existential threats to that extent.

This alignment in both timescale and focus-scope between an innovation org an its money factory. See [[§Startup Constraints]], [[§Corporate R+D Constraints]], [[§Philanthropy and Non-Profit Constraints]], and [[§Academia Constraints]] for all the lovely ways the misalignment can happen.

## Related
- [[gertnerIdeaFactoryBell2012]]



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Large companies use startups as outsourced R+D

# Big companies cannot start small things
Say you’re a company with $100B in revenue. (Google’s was $136B in 2018 and Apple’s was $258B) A two year old startup that is utterly crushing it is going to have an ARR of $100m. That’s a tenth of one percent of the total revenue after two years. They’re just totally different worlds.
Unofficially, some unnamed internet companies won’t even consider new products if there isn’t a strong argument that they can get to $1B revenue and/or 100M users within 6months to a year. Now, this might be possible because they already have the sales channel to billions of people, but it means that they would never support a niche product that will take a long time to take over the world.



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§Corporate R&D Constraints

DARPA provides a derisking role for people in other organizations

Corporate venture funds allow multinational corporations to dabble

# Companies want R&D to be off of their balance sheets

Questions
- [[Why do companies want R&D off their balance sheets?]]



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# Corporate R-D orgs experience a tension between Research and Development
Research is associated with scientists, while development is associated with engineers. Those two different roles have deep cultural implications going back to the 19th century and before where scientists were brilliant creators of knowledge and engineers were basically mechanics who tightened bolts on trains and in factories. WWII didn’t help the matter, the public perception being that the scientists who distilled the secrets of the radio wave and the atom were the ones who won the war.  

These cultural associations created significant tension even at [[Bell Labs didn’t have a unique model]], where the researcher was a coveted position and were outnumbered by engineers 10 to 1.  The implication even know is that the researchers do the hard work to discover something and then hand it off to developers to muck around and turn it into a product.  Do a gut check on what your respect level for someone would be if they told you that they were a “researcher” vs. a "developer.”

This plays out like a fractal version of the [[At the organizational level corporate R&D labs either cause brain drain or don’t have the best people]] where everybody inside the org wants to be a researcher. 
Some organizations deal with this by separating the 



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There is a significant class of innovations that would create drastically less value for the world if their value had been captured by their creators

It is hard to capture value from research

Most tech transfer offices are not profitable

Value capture does not always need to happen through IP

Creations whose impact depends on being public generate uncertain value through not-particularly modular combination with many other things and have a large combinatorial surface area

It is nebulous a priori which research should produce public goods vs private goods

A normal for-profit structure would hamstring an ARPA-riff

Better value capture mechanisms

Profit is important for both practical and ideological reasons

PARPAs ideal legal structure comprises both a nonprofit and a for profit

There are many questions and issues with the idea of pay-it-forward tithing

ICT  Reviewers

Omniscience is not enough

# Current value capture mechanisms are crude
The direct ways to capture the value from research are to patent[^1] it or build a product around it (and leverage trade secrets.) Other possibilities are more indirect and ‘credit based’: consulting, speaking fees, or leveraging reputation to get more lucrative positions later.

### Patents
Patents require an expensive application that can take years to process, lawsuits to enforce, and effort to monitor for violation, and lead to an extremely illiquid market. On top of that, there is tons of valuable work that just isn’t patentable.

Capturing value with a patent is often inefficient. In an ideal situation, someone building a product that incorporates the patented process comes to you, the two of you negotiate a licensing deal, and they send you a check every year. The world is not ideal. Often, you need to *find*people who are violating your patent and threaten to sue them if they don’t pay up. Standard lawyer advice when building something is to *not* look for prior art because it decreases your liability in case you happen to reinvent something. Even if someone *does* come to license your patent, the negotiation over licensing terms can be brutal - there are no standard terms and especially for a startup it can create a drag chute on a product’s profitability that makes an already hard job even harder. Once the licensing agreement is signed, you still need to make sure that the company is paying you the royalties you’re due and sue if they’re skimping you. [[Many inventors have spent a lot of their lives in court over patents and sometimes wind up dying in the gutter in spite of that]]. 

It’s worth noting two historical facts about patents that shed some light on their limitations.  [[Patents have become more BS over time]]. On top of all of that, patents weren’t even created as a value-capture tool for discovery or invention.  Instead, patents started as a way for monarchs to increase their income without going through Parliament by granting temporary monopolies.[^2] Of course, the modern patent system is explicitly meant to incentivize innovation so this might be nothing more than a cool anecdote. 

Additionally, [[Patents inherently prevent a piece of IP from being combined with other knowledge]].  The combination-preventing nature of patents is particularly problematic if you want to use them to try to fund work to address gaps in work on processes and systems (which naturally require technological combinations). <[[In the past, corporate labs filled a particular niche in the innovation ecosystem]]>

### Products
The first reason products are a crude way to capture value from research is that most modern products are an amalgamation of different technologies and designs. So while a piece of research may have vastly improved a component, that doesn’t necessarily translate to a vastly superior product. 

Second, products are inherently coupled to companies, so the success of a product (from a value capture standpoint) is inherently tied to the success of a company. This coupling means that there are many other factors that affect the product’s success besides the product itself (let alone the research-based piece of it). Markets, sales channels, margins, fundraising, perception, etc. all make or break companies independent of their products.  [[The best technologies don’t always win]] is an adage for a reason.

“But unique new technology creates a moat that will make the company successful!” Jerry Neuman’s article,[[Productive Uncertainty]] argues convincingly against pure-technology with the idea of “excess value” an invention would produce for a startup. 
`If a moat exists prior to the startup being founded (say, a patent) then, absent uncertainty, this patent could be sold for at least as much as the startup could garner from it.` 

More broadly, value capture through products force innovations to be subject to either [[§Startup Constraints]] or [[§Corporate R&D Constraints]]. None of this is to say that products are a *bad* way to capture value, only that they are subject to many constraints and contingent requirements that mean that the correspondence between valuable technologies and technologies whose value can be captured by a product is weak. 

### Related
- [[Michael nielsen re david d value of creative work twitter]]
- [[Patents held back aviation]]

[^1]: I’m going to just talk about patents but the same arguments apply to both
[^2]:Anton Howes <[[Age of Invention- How to Build a State]] >



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# Healthy R&D orgs feel like they have very smooth project rampups with high ceilings
# Healthy R-D orgs feel like they have very smooth project rampups with high ceilings
Anecdotally you have stories about people putzing around on something for a while ([[Ideally corporate R&D enables targeted dicking around]]) and then roping in a few people part time, and if it shows promise going from there. In this way R&D labs seem like the only institutional structure that allows research to start small and then ramp. [[It is important for big things to start small]]. 

This smooth ramp stands in contrast to academic labs where you can start small but you will hit a scope ceiling quickly outside of a few exceptions because of the size of grants and the individual publishing imperative. [[Academia is not a good environment for systems engineering]].  Grant-based funding in general seems like it makes smooth ramping hard, so national labs would seem to suffer from the same problems. 

Small businesses don’t have the same ceiling as academic labs, but it is hard for them to scale smoothly, unless the work is smoothly coupled to profits. Unfortunately, some of the most interesting work leads profits, so in order to go after them a small business would need to transform itself into a high growth startup. Startups have the opposite problem, where they have a hard *lower* bound on size and smoothness. They’re expected to scale quickly - if a startup is not constantly hiring it’s a red flag. 

Ideally, the combination of academic labs + startups should enable this smooth ramping, hopping from academia to startups. However, there is often a gap between where you can get in a lab vs. what you need to start a startup. Hence [[The (idea) valley of death]].  It’s worth spending more time thinking about the ‘dimensions’ of the valley of death - because it’s not a matter of number of people as the “ramp” language here might imply. 

It’s informative to contrast [[Google Brain]] and [[Google X]] in the context of smoothly ramping projects. Google Brain feels like it is a place where there’s a ton of different things being tinkered on at once, with projects of all different scales. [[Modern AI R&D labs feel like corporate R&D labs of yore]]. Google X feels like there’s actually little tinkering before either launching into a full-scale flagship project or killing the idea. X famously prides itself on ‘killing ideas quickly’ which is a good move for startups but perhaps squanders the advantages of CR&D. 

It would be interesting to plot a histogram of project sizes at different organizations. My hypothesis is that healthy ones would have a nice smooth decay - lots of little projects and a few big projects and many in between. I suspect that many organizations would have a hole in the middle. Maybe that’s [[The (idea) valley of death]] appearing as a statistical phenomena. 

### Related
- [[Ideally corporate R&D enables targeted dicking around]]
- [[Ideally corporate R&D enables work on a general purpose technology before it’s specialized]]
- [[§Corporate R&D Constraints]]

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# Internal incentive structures within a large organization work against high quality R&D labs
# Internal incentive structures within a large organization work against high quality R-D labs
Abstractly: [[Organizations reward people for creating new successful products]] so people are incentivized to create something successful. That incentive wouldn’t necessarily preclude working on something with a high chance of failure *but* even if you accomplish something massive, it won’t move the needle a lot at a large organization and you won’t capture a ton of it’s value . ([[The returns to politics is higher at large organizations - Stake v Rank]]). If the only incentive at a corporation is around expected value creation, people will be disincentivized to work on long term high risk projects. 

A  successful corporate (or other) R&D lab requires creating separate incentives from the rest of the org. [[Separate soldiers and scientists]]. My hunch is that modern high-feedback, metrics driven cultures do not do this well. 

The big question is [[What changed from the high-quality corporate labs in the past?]] 

### Related
 * [[Organizations reward people for taking over large projects]]



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Brains are wombs for heuretics

# It is easier to have a multipoint mutation in technology than in evolution
A multipoint mutation is analogous to [[Systems must take performance hits to get out of local optima]] - in the tech tree model this means having one or more low impact precursor nodes.  

Almost by definition you need multipoint mutations to fix [[Structural Constraints]] because structural constraints mean that there are two or more interlocking dependencies. 

In biology, it’s (impossible?) to have a precursor with a single mutation that renders it inviable give rise to a precursor that has a second mutation that addresses issues from the first and builds on it. We’ve used genetic engineering and mutation breeding to get around this fact. ([[What does technological genetic engineering look like?]] and [[What does technological mutation breeding look like?]])

In technology a non-viable (non impactful) precursor can be kept alive in a lab or in an obscure paper somewhere. These are unexploited [[Leonardo Ideas]]! Which may give a hint that the process of finding unexploited Leonardo ideas ([[Leonardo Ideas can be systematically identified]]) may be part of the answer to [[What does technological genetic engineering look like?]]

 - It is hard for startups to address multipoint mutations because it takes a long time and its hard to sell as a product [[§Startup Constraints]]
 - It is hard for corporate R&D to address multipoint mutations because people often have vested interests in the old system. [[§Corporate R+D Constraints]]
 - Academic research often creates proposals for multipoint mutations but because [[Academics are incentivized to do peer-reviewed publishable work]] they have no incentive to do implementation. [[§Academia Constraints]].
 - Why does 

### Related
- 



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The linear innovation pipeline is more harmful than useful

Innovation requires multiple heuretics

PARPA will address several failure points

# Many innovations are not valuable on their own but can be combined to be valuable
This could either be within the process of a larger organization or with other completely new things.
Examples: Touch keyboards are almost worthless without really good autocorrect
This is one of the places where corporate R&D may shine 
## Questions
[[What are examples of technology being combined well?]]
[[Innovations that are not valuable on their own but could be combined well to be valuable?]]
## Related
[[§Corporate R&D Constraints]]
## References



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Different institutions have created monopolies on various enterprises

Marissa Weichman Conversation 2020-04-10

# National labs and other government research centers are a research alternative to universities
Government researchers are still in the ‘academia’ game in that they want to publish papers and discover and invent cool shit.  [[People and organizations are all playing some game that has different ways of gaining status and power]]

Government researchers have both less freedom and more freedom than university researchers. If they can justify how their research is aligned with the higher-level missions of the organization, they don’t need to spend time writing grants or teaching like professors in universities. [[Professors do three or more jobs]]. The need to spend the majority of their time on organizationally aligned research means that they can’t do super crazy things. Additionally [[The people setting research priorities at AFRL are often not technical]], which is another constraint that may kill promising projects.

Research at national labs is perceived by people in academia as being lower impact than at universities. [[University professorships are the highest status jobs in academia]]. The lower impact is because their research needs to align with the department they are part of. ([[Innovation orgs need to be aligned with their money factory]]. This creates a feedback loop where the really good researchers don’t work at government labs and the phenomenon of [[A players hire A players and B players hire C players]]  happens.

This whole story is so similar to [[§Corporate R+D Constraints]], I would go so far as to say that government labs are basically tax-payer funded Corporate R&D.  [[Government research is corporate R&D for United States Inc]].

Questions about the orgs:
- [[How are research agendas set at National Labs?]]
- Are the people who work there technically government or university employees?
- How is the money allocated
- How does your career progress?

### Related
- [[Federally funded research and development centers - FFRDC]]
-  [[Frederick National Laboratory for Cancer Research]]
- [[Air Force Research Laboratory - AFRL]]
- [[Naval Research Laboratory]]
- [[Jeff Graham Conversation 4-17-2020]]

[^1]:// According to [[Marissa Weichman]] :



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# Problems that require system-level solutions often require massive teams
This is especially true when you’re talking about systems *within* an organization - you need to replace-in-place, backwards compatibility etc.
This may be one of the [[§Corporate R+D Constraints]] and a reason why [[Corporate R+D orgs are expensive]].
From [[Conversation with apple research guy 12/2018]]



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It is hard for a private ARPA to capture value without hamstringing impact

# Profit-maximizing organizational structures can hamstring technologies whose impact depends on them
Profit maximizing organizations obviously have incentives to try to capture as much of the value they create as possible. Of course, profit maximizing companies are not mindless greed-monsters. In many situations, giving up short term profit can actually lead to longer term revenue. Ideally, these organizations would be able to sit at some point on the tradeoff curve that maximized value *by* maximizing impact. Keep in mind that profit-maximizing organizational structures can also be the best way to support a technology! [[tensions]]. 

However, there are four mechanisms that can lead to good-acting but for-profit companies hamstringing technological advancement.

*Specialization*.Generally, it’s a good idea for a for-profit business to start with a single product and a specific market or niche. This good business practice means that short of a large company with an R&D department, most of the development work should be focused on productizing a technology for a specific task. Startups who want to *start* by building a platform or doing all the things tend to go down in glory-seeking flames. Specializing technology for a specific task is not inherently a bad thing! It’s also possible to thread a path of increasing generality, starting from a small niche and jumping to bigger and bigger applications over time. Elon Musk is the master of this. However, it’s just as possible to get stuck in one of those niches, especially if it’s not profitable enough to support R&D on the more general technology. This situation happens all the time with university spin-off technology, where it really needs more general development to reach a certain performance threshold, but it gets stuck in a niche in order to build a company.  <Need examples.>

*Investor Pressures* For-profit companies working on technology generally need investors because they won’t turn a profit for at least several years after getting started. These investors have their own incentives, and maximizing the impact of a technology generally is not the most important one. Investors can have specific timelines for an exit, incentives to show growth in order to raise a new round, or just opinions about where the technology should go that conflict with maximizing impact. (Of course, any source of money comes along with its own set of pressures!)  

*Capturing value* For-profit companies need to capture some value that they create! [[tensions]], but [[There is a significant class of innovations that would create drastically less value for the world if their value had been captured by their creators]]. 

*Speed* An organization could realize all of the above traps and forgo trying to maximize profit, not take investment, and make most of its technology work public, pulling in revenue through non-exclusive license fees and consulting work. Unfortunately, this strategy can hamstring the technology you’re working on in a different way, slowing general-purpose development to the work that can be done when you have particularly lucrative contracts. Many aerospace technologies seem stuck in this particular mechanism. 

It is rare that an organization can both forgo significant value by making key parts of a technology public *and* make money hand over fist at a rate that satisfies investors and can fund the development of general-purpose technologies. 

### Related
- [[It is hard for a private ARPA to capture value without hamstringing impact]]
- [[§Startup Constraints]]
- [[§Corporate R&D Constraints]]
- [[It is hard to capture value from research]]



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Inverting the normal relationship between an R+D organization and is money factory may enable new classes of activities

The siloization that tends to happen in large corporations hamstrings applicability and serendipity

# R&D orgs are always on the side of an org chart - The lab is always separated from the mothership
# R-D orgs are always on the side of an org chart - The lab is always separated from the mothership
This is a completely rational choice. R&D orgs don’t make products, therefore they don’t bring in profit for an organization, therefore they are a cost center and should not be at the center of an org chart.
Noted in [[The Goal]]
This is one of the reasons [[PARPA will address several failure points]]



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Ideally corporate R&D enables targeted dicking around

# R&D orgs are answerable to shareholders both in scope and timescale
This is true in both public companies <need more evidence here> and private companies in terms of their VCs. 



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Physical experiments are expensive

# R&D orgs are expensive
My perception is that R&D orgs are not the most monetarily efficient places.
Why are R&D Orgs expensive?
- Part of the inefficiency could be tied to the[[At the organizational level corporate R&D labs either cause brain drain or don’t have the best people]] - people can’t capture as much of the value they create as if they were in a startup so you need to make things very comfortable.  
- The best people are expensive.
- [[Problems that require system-level solutions often require massive teams]]
- Because R&D Orgs are often not aligned with the parent org there isn’t a lot of focus, which means that you just need more money. This is not necessarily bad. 
- I suspect a lot of good research gets thrown away because there are no default pathways for the research to become part of the org.
Need stats on this 
- [[Microsoft Research]]
- [[Bell Labs didn’t have a unique model]]
- [[Xerox PARC]]

                        

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# Research programs that are constrained from existing
Some baseline assumptions:  nobody in the system is an actively malicious actor, and nobody is making stupid choices. Under this framework, incentives are the most important thing to pay attention to. 

The major institutional players are
- Academia
- Non-profits  (Which break down into charities and foundations)
- Incumbents 
- Startups
- The Government

The things that don’t seem to be happening are specific combinations of the following factors (note that any *one* of these is insufficient - it’s the combinations) 
- High Externality 
- High coordination
- High complexity
- Physical
- Potentially paradigm shifting

### Related
- [[DARPA works where markets dont]]
- [[Market failures are actually failures of the existing institutions to allow markets to work]]
	- [[§Academia Constraints]]
	- [[§Non profit constraints]]
	- [[§Corporate R&D Constraints]]
	- [[§Startup Constraints]]
	- [[§Government Constraints]]
- [[All the incentives in research drive towards conservatism]]
- [[Incentives are the fundamental forces of peopleland]]



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Bell Labs and AT&T had timescale alignment

The timescales of invention and discovery are high variance

Long-term projects are inherently highly levered up

# Rewards in a big company operate on one timescale and R&D happens on another timescale
# Rewards in a big company operate on one timescale and R-D happens on another timescale
 At the end of the day, the incentives in the org act on[[Human timescales]] - promotions, career progression.

Politics happens on a shorter timescale than results, and the real impact of a piece of research may be on an even longer timescale than a single career. 

These wildly mismatched [[Buxton Index]]es throw a wrench into R&D. 

### Related



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dijekstraStrengthsAcademicEnterprise1994

# The boundaries between industry government and academia are fuzzy
Usually the fuzziness comes from one group paying for research that another group is doing. 

Grad students and post docs work in government labs, like [[National Institute of Standards and Technology - NIST]] or the [[Naval Research Laboratory]].

Academic labs work on corporate grants - you can see this in everything from subcontracting on government projects to the origins of Genentech. [[Genentech was started by bringing together several pieces of existing research and subcontracting to university labs]]

Government labs like [[National Institute of Standards and Technology - NIST]] work on corporate projects through grants the same way they work for the government. 

Corporate R&D works on Government grants all the time. 

In this whole structure, [[People giving out grants try to derisk them as much as possible]] so it is more likely that a more ‘established’ institution will get the grants.

### Related
- [[It is difficult to get a man to understand something when his salary depends upon his not understanding it]]
- [[§Academia Constraints]]
- [[§Corporate R+D Constraints]]



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A large part of a DARPA program manager’s job is focused network building

§ARPA model

ARPA became DARPA in 1972 because of the increased scrutiny on military spending both in the government and outside of it

It is relatively easy for DARPA PMs to re-deploy funding

fuchsRethinkingRoleState2010

Was the shift from ARPA to DARPA a focus change or a process change?

The majority of changes in the ARPA model over time have involved adding more explicit process

How was DARPA structured before the 1970s?

DARPA’s Process for Creating New Programs

# The definition of the ARPA model has changed over time
` “There is not and should not be a singular answer on ‘what is DARPA’—and if someone tells you that [there is], they don’t understand DARPA”` - [[Richard Van Atta]]

It’s important to lay the changes out and discuss whether or not they affected the change in DARPA’s outlier output. Interestingly [[The majority of changes in the ARPA model over time have involved adding more explicit process]]. 

The biggest change happened when [[ARPA became DARPA in 1972 because of the increased scrutiny on military spending both in the government and outside of it]].  Which raises the question [[Was the shift from ARPA to DARPA a focus change or a process change?]]

 It’s tempting to throw out anything introduced since 1972 unless it was a codification of a previously implicit rule.  For the most part, it is a good idea to [[Pay attention to DARPA’s informal process and ignore formal process]]. However, the world has changed since 1972 ([[WTF happened in 1971? - Article]]) so it’s worth considering whether some adjustments were made to enable DARPA to more effectively operate in the world.   

### Additional Process Changes
- Some time before 1993 shifted to a three phase development system. 
	- **Ignore - additional process**
-  [[The Heilmeier Catechism]] was introduced in 1975, so it did not contribute to any of the output in the 60’s or early 70’s. In fact, [[cummingsARPAPARCDream2018]] attributes the decline in output to its structure and an implied focus on results.  There are arguments both ways, Many explicit DARPA rules like transient program managers are just codifications of previous implicit rules so the catechism may have been an informal rule of thumb test. Additionally, Heilmeir himself was an engineer who also discovered phenomena that enabled liquid crystal displays so he presumably understood the process of true invention and discovery. On the *other* hand, he worked on planning for McNamara so he very much bought into a top-down mentality. 
	- **Ignore - additional process** Though it seems like it could be useful as a thinking framework as long as it isn’t used as a bludgeon. 
- Currently, DARPA managers do go/nogo checks every few months on performers. From stories about [[JCR Licklider]] program managers used to exert less control on performers.  I suspect that the ability to move money quickly away from things that aren’t working is important though. 
	- **Ignore - additional process**
- In the 1960’s DARPA program managers didn’t didn’t do open solicitations for grants through an official government system - they just gave money to whom they wanted.  [[Restrictions on spending money happen when you reach trust limits]]
	- **Ignore - additional process**
- In the 1960’s it wasn’t official policy that[[DARPA Program managers have a tenure of four to five years]] but the official policy was just a codification of the informal rules when Program Managers started hanging around too long.  
	- While technically it’s addition process, this is just a codification of an informal rule 
- Currently, [[Every program at DARPA is intensely technically scrutinized by the tech council]]. In the 1960’s the process was a quick meeting between the Program Manager and the director laying out what they wanted to do.  
	- This one is tricky - from conversations with program managers, the Tech Council sounds useful because it enables a clear goal for PMs without restricting what they do. [[The ‘framework’ DARPA PMs use to create a program is by modeling the tech council]]
- In 2001 DARPA changed who can be a prime contractor from anybody (including research labs and startups) to only large companies.
	- This one is also tricky - [[fuchsRethinkingRoleState2010]] makes the argument that this is an adjustment for the fact that Corporate R&D has declined and it’s hard for startups to get products into production so one of DARPAs new roles is almost as a systems manager. 
		- [[Government research is corporate R&D for United States Inc]]
		- [[§Corporate R&D Constraints]]
		- [[Companies want R&D to be off of their balance sheets]]
		- [[Old school Pharma VCs add value because they help startups go through an established sales process to pharma companies]]
		- [[Frontier tech has bespoke sales channels]]
		- [[Manage the Transfer not the Technology]]
### Other
- [[Starting in 2001, DARPA directors began to sync up with presidential administrations]]. This suggests that there’s been a shift in the politicization of the organization. 
	- Obviously this seems bad.



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People are professionally motivated by money, glory, autonomy, or impact

The statement ‘it’s all about the best people’ is either a suitcase handle term or lazy thinking

Unique impact is the reason that many people stay at NASA

# The incentives inherent to corporate R&D mean that researchers motivated by money leave and those who are motivated by glory are not the best in the world
# The incentives inherent to corporate R-D mean that researchers motivated by money leave and those who are motivated by glory are not the best in the world
The ‘best people’ usually know their market value so by default they can capture more monetary value by spinning out their own thing or going to academia where they can capture more glory. The exceptions to these may be when doing corporate research relaxes constraints. 
This assumes [[People are professionally motivated by money, glory, autonomy, or impact]].
*Money*
If an individual researcher is motivated by money and create something valuable, they can capture more of the value they create if they spin off a startup.[^1]  Therefore, researchers who stay in R&D labs long-term aren’t motivated by capturing value. 
*Glory*
Researchers can capture more glory because companies have the incentive to keep many more things secret. In academia, the only thing that keeps people from publishing is fear of being scooped. In a company you want to keep the research secret until you are already in a position where you can execute on the research better or you have no interest in executing on the research. Therefore, you would expect researchers who are doing things  relevant to the company’s core business to have restrictions on what they can do and therefore less publishing and glory-capture than if they were in academia. 
One exception to the ‘glory-restrictions’ on business-adjacent research may be if the company feels like they’re the only ones who can take advantage of the research. This suggests that one of the reasons [[Bell Labs didn’t have a unique model]] may have been able to publish so much was because their monopoly position assured them they would be the only ones able to execute on the research.  [[Google Brain]] may fall into this same category - they have an effective monopoly on search and video hosting. 
*Autonomy*
The “something else” could be doing interesting or novel work that would otherwise require months of grant writing. <[[Months of grant writing is one of the things that makes people think twice about academia]]> However novel, interesting work is often not aligned with the parent org because the value of innovations is not always clear and[[Big companies cannot start small things]]. If research doesn’t fit into an existing business unit, only outliers are incentivized to keep it going which means that researchers in corporate R&D can only be autonomous for a finite amount of time.
*Impact* 
Corporate R&D can potentially have large impact *if* you’re working on something that’s aligned with the main company. For example if you’re working on research that can become part of an existing Google product, it will affect billions of lives. However, to sound like a broken record because [[Expensive research needs to address an existential threat eventually at an organizational level to maintain support]], anything research not connected to a core product will have a finite lifetime and probably won’t have a large impact.
One way that Corporate R&D *can* get the best people is if they provide resources that would be hard to come by otherwise - the massive compute at [[haoMessySecretiveReality2020]] and [[Google Brain]] for example, or access to world-class labs at pharma companies. 
The incentives to get the best people to stay may also be part of the reason why [[Corporate R+D orgs are expensive]] - you need to provide them with hard-to-get resources, which usually translates to $$. The other way to keep the best people is to make them feel exceedingly comfortable - call this a ‘local optima’ - which is also expensive. 
(Of course [[Bell Labs is one of the few examples of an innovation org that was aligned with its money factory]] for very specific reasons)
[^1]: This is especially true in the world of software but may hold less true in areas with higher capital costs.  Also depends on the company’s willingness to acquire. [[Are there ideas that create a lot of value for a big company but would make terrible startups?]]
## Related
 [[Innovation orgs need to be aligned with their money factory]]. 
## References
* [[Conversation with Charlie Songhurst 12/2018]]
* [[Conversation with Alex 7/1/20]]


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# The siloization that tends to happen in large corporations hamstrings applicability and serendipity
Large corporation org charts are very hierarchal. This hierarchy is necessary for people to put their heads down and get work done but it has one of two effects: either you put all the research in one part of the org or you scatter it throughout the business units. You end up trading serendipity for applicability.     
If you put all the research in one part of the org, you maximize serendipity because those researchers can interact with each others and people from other parts of the org can more easily see all the research. On the other hand it leads to all the problems when [[R&D orgs are always on the side of an org chart - The lab is always separated from the mothership]]
You might think “aha! The solution is to put researchers in each org, so they’re sitting next to the people who will use their research.” This approach solves the applicability problem but significantly decreases serendipity: it’s hard for one org to know what research is going on in another org. 



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# There are several macro-economic analyses of both why and whether there has been a decline in corporate R&D
* [[Killing the Golden Goose? The changing nature of corporate research, 1980-2007]]
* [[Is there R&D spending myopia?]]


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

PARPA needs a constitution with checks and balances

Mark Johnson the Elder Conversation 4 Jan 2021

Possible PARPA Missions

# Things that are not paper-worthy enough for academia and not product-focused enough for a startup
There are many heuretics (discoveries/inventions/innovations/ideas etc.) that don’t fall within the constraints of the institutions we expect to support them. “Things that are not paper-worthy enough for academia and not-product focused enough for a startup” is shorthand for one class of these heuretics. Note that there are many others! 

First, let’s address some misconceptions about what falls into this category.  Wacky ideas and long-term projects are two things that many people assume don’t get supported in universities or startups. This is not true.
	- Long-term projects get support *if* they hit consistent milestones *and* they’ll be able to capture a lot of the value they create if they succeed. 
	- Wacky ideas get support *as long* as they don’t threaten paradigms too badly *or* someone thinks they can make money off of them. 
	- Research projects that require a lot of people or coordination *as long as* the final product is a paper they can all put their names on. See the LHC. 
	- Research projects that require some engineering effort *as long as* the engineering effort resembles something that has been done before: building a bigger telescope/particle collider/linear accelerator/space probe, etc.

The reasons that ideas are not paper-worthy or product-worthy *make sense* given the incentive frameworks in their relative institutions.  [[Most people are not malicious or stupid]]. 

### What makes something not paper-worthy?
‘Paper-worthy’ is meant as shorthand for ’things academics are incentivized to do.’ So things that are not paper worthy are those that don’t obey [[§Academia Constraints]].  It’s important to explicitly call those implications out because that call-out can shine a light on where to dig for hidden gold. 

1. **An idea isn’t paper worthy if not considered “novel enough” or “not a contribution to the literature” by peer reviewers.** [[The word ‘novel’ is used as an idea bludgeon in academia]] and weeds out many classes of ideas. 

A non-exhaustive list of what gets shot down by peer review: 
- New approaches to old “solved” problems, 
- Better implementations of an existing idea that’s already been implemented, 
- Efficiency improvements along a different metric than the ones the field normally cares about, 
- Scaling existing techniques, 
- Creating something for a very specific application as opposed to a general principle/purpose, 
- Further work on things that have already gotten to proofs of concept
- System integration of several already-existing ideas.

For more see:  [[Academia is not a good environment for systems engineering]]. [[Academia incentivizes novelty, not focus]].  [[Academic research is often just the concept half of creating a heuretic]]. 

2. **An idea generally isn’t worth writing a paper about if it’s not expected to get a lot of citations.** Even if it’s published, low-citation ideas languish in obscurity. It’s valid to ask ‘are there things that are valuable that don’t get a lot of citations?’ 

I would argue yes, valuable projects that wouldn’t get many citations include:
	- Projects that are intermediate steps to a larger vision without a strong conclusion besides “we did a thing!”
	- Engineering work in a theory-heavy field or theory work in an engineering-heavy field.  More generally, work in a field that the field doesn’t think is valuable. 
	- Ideas that are far enough out of mainstream won’t get many citations because people don’t have the bandwidth to dig in and see if it’s worthwhile and the default answer is “no” (see #3 below) 
	
3. **It’s hard to publish ideas that are too outside of the approved way of doing things.** [[Paradigms outline a set of questions and the rules around answering them]].  Ideas can go too far outside of accepted paradigms when when you’re asking different questions, or measures success differently than standard ways. [[Antedisciplinary Science]] often falls into this trap as well, when it’s too much like discipline X to be published in a journal for discipline Y but it’s too much like discipline Y to be published in a journal for discipline X.  This situation is of course tricky because many (most?) ideas that go outside of paradigms are legitimate crackpottery. [[Anyone on the knowledge frontier is a bit of a crackpot]]. 

4.  **It’s hard to write a paper if you can’t even get the money to do the research to write the paper.**
The requirement to align with current funding priorities is particularly pernicious because [[The government grant process depends on politics and committees]]. Additionally, one reason to write papers is to continue playing the game, so if a paper doesn’t hint at your continued ability to do work that aligns with funding priorities, you’re incentivized not to work on it. [[People giving out grants try to derisk them as much as possible]].

5.  **A project is unlikely to lead to a paper if it’s not within scope for a PhD student or untenured professor.** 
[[Graduate students are the labor in academic science and engineering]]. In exchange for cheap labor, grad students expect to produce research within a 4-7 year time frame that can help them move on to the next rung of the academic career ladder. (Despite the fact that [[There is an increasingly wide gap between the number of graduate students training for PhDs and academic positions for them to fill]].) While a tenured professor might be willing to work on something crazy that will take 10 years and might be a complete dud, no grad student would rationally work on it.  The incentive for PhD students to produce high-impact papers for the sake of their careers serve to amplify all the other constraints, and add the timescale constraint. Additionally, PhD students in science get most of their funding via research grants, so research they work on is especially susceptible to #4. 

6. **Projects that produce a lot of structured data or reusable code don’t led themselves to papers.** Academic papers are built around the paradigm of scientific inquiry, where collecting data and writing code are only in service to producing an abstract model or theory - the more generalizable the better. There is little reward to producing high quality, reusable datasets or code, despite the value they can create. 

7. **A project is unlikely to lead to a paper if it involves a lot of coordination.** Academia emphasizes individual agency. This is a good thing, but if a project cannot be effectively modularized it will end up requiring a lot of grungy coordination work that doesn’t count as a contribution unless you’re in charge of the project. A big reason to go into academia in the first place is to avoid having a boss and because you like doing your own thing, your own way. Obviously there are exceptions like the LHC, but that is a situation where the experimental particle physicists are locked into a paradigm and have no other option. 

Coordination aversion hits hard projects that don’t fit into a particular disciplinary bucket or require integration between components built in different labs. Projects of a particular scale require coordination by their very nature. See[[A new structure for scalable research]] for a much deeper dive.  

### What makes something not product-focused enough for a startup
‘Product-focused’ is meant as shorthand for ’things startups are incentivized to do.’ So things that are not product-worthy are those that don’t obey [[§Startup Constraints]].  It’s important to explicitly call those implications out because that call-out can shine a light on where to dig for hidden gold. 

1. **There’s no convincing story for how the project’s output produce a massive and competitive ROI on a reasonable timescale for a VC-funded startup** 
*Massive ROI* Venture capitalists need to believe that any investment in their portfolio could potentially return the fund. 
*Competitive ROI* Investing isn’t just about absolute returns - any investment is in leu of another investment. So if the stock market is absolutely crushing it, there’s less incentive to invest in higher risk technology. Even within the VC realm, many atom-based technologies have the same risk or higher than a software startup but expected return is lower (especially when you take timeline into account) or farther in the future. From the cold hard numbers SaaS is often just a better investment.  
*Timescale* [[J.C.R. Licklider]] started work on the ARPA project to build what we would consider “modern computing” in 1962 and perhaps the first real ‘value capture’ event based on it was Apple’s IPO in 1980. [[Venture capitalists need to fund things that increase valuation in a few years and return in 5-7 years]] so this trajectory would have been untenable as a startup. Venture timescales often lead to projects being pushed to focus on a specific application or being acquired before achieving the technical goals they set out to hit. There is nothing wrong with either of these routes per se, but they can lead to technologies failing to live up to their potential. 
2. **It will take a lot of development work to sell anything for a bootstrapped startup**
Startups do not *need* to raise money from VCs. However, that does mean that if the founders are not extremely wealthy ([[SpaceX]], [[Blue Origin]]) the startup needs revenue early on. If the project will take a lot of development work before it can sell anything the startup needs to get revenue somewhere. Some bootstrapped startups use consulting work to create revenue to fund development. Unless it’s wildly profitable, the consulting approach severely limits the amount of time and money that can be spent on development, possibly dragging it out forever. [[Some projects need effort above a certain threshold to move forward]]. [[SBIR]] grants can provide another option, but they are relatively small (order $1m over several years.) 
3. **It’s hard to capture the value the project creates**
Markets are great, but there is no law of economics that people can always capture the value they create if they are clever enough. (See [[Schumpeterian Profits in the American Economy - Theory and Measurement]].)  Research often falls into this trap because [[Innovation requires multiple people]]. Often the person who figures out how to make a lot of money off of a discovery or invention is not the person who created it, nor are they even in the same organization.  “But what about intellectual property ?” There are *many* reasons intellectual property does not ensure everybody involved in an innovation capture the value they create but a short list includes: many people in many organizations contribute to most innovations, patents only work for a narrow set of innovations, and patents have massive downsides - including potentially capping how much value an innovation can create. 

A more subtle issue than the question of value capture itself is that trying to capture the value created by an innovation can severely hamstring the total value it creates. Consider the [[Graphical User Interface]] and other personal computing innovations created at [[Xerox PARC]]. Arguably they’ve created trillions of dollars of value for the world. Would that be the case if Apple had a patent on the GUI and mouse? Probably not. This situation is not unique. 

[[It is hard to capture value from research]]

4. **The project is changing one or more components of a bigger system or process** 
A startup that is trying to improve or change a component of a bigger system needs to either convince whoever runs that system to adopt the change (which is especially hard when no particular entity runs the system) or the startup needs to build the entire system themselves with the change incorporated. 

Obviously, the change needs to happen eventually, but many paradigm shifts (especially in complex systems) initially *lower* performance, at least on traditional metrics. [[Metrics can cause paradigm lock-in]]. Change happens either when the new paradigm gets enough reps to catch up to the old way of doing things or people see the new paradigm in action and accept that the traditional metrics weren’t capturing all the important features of a system. Both of these situations are hard for a traditional startup to achieve because both approaches take a lot of time and resources before showing ‘traction’ and the potential customers (the people running the systems or consuming their outputs) will assert that the idea is dumb up until its not. 

See [[fillerFundamentalManufacturingProcess2020]] for more.

5. **Innovations that can’t be encapsulated into a product**
There are some innovations that it’s just hard to productize. Process innovations often fall into this category - especially those that don’t depend on new technology per se, but on a new way of doing the same thing with basically the same tools. The [[Bessemer Process]], for example, used basically the same crucibles but you put different things in them. Social process changes (like Kaizen[^1]) are even harder to productize. 

In these situations, the vast majority of the value generally accrues to the end-product producer or consumers. The naive advice would be “start a firm that’s doing the end-to-end thing” and outcompete the incumbents. While that should theoretically work, it often runs into the reality of extremely complex products that you would need to reinvent from the ground up (like if you had a better way of making passenger jets), heavily regulated industries where it would take massive lobbying just to use a new process, and other scenarios. A startup doesn’t usually have the time or resources to tackle these scenarios. The alternative approach is to take on a change-management consulting role, which usually doesn’t capture enough value to be VC-fundable so it needs to be profitable early on. Additionally consulting runs head-long into the issues in point #4 above. 

In some ways this point is intertwined with #4. 

6. **Startups shouldn’t tackle market, channel, AND technical risk** 
New technological paradigms usually come with all three of technical, market, and channel risk. You simultaneously need a lot of work to get the technology to a point where it’s competitive with alternatives, to figure out who wants to use it, and to establish how they’re going to buy it. 

It’s common wisdom among VCs that it’s not a good idea to invest in companies that are taking on two, let alone three of these risks. This heuristic isn’t misguided.  The chance of failure goes up *dramatically* when an organization tries to tackle tow, let alone three of those risks. Startups should only tackle one (or *maybe* two) of these.  The reason therapeutics focused startups can exist is that they have almost no market or channel risk - there is a well established pipeline to being acquired by a pharma company and insurance companies are guaranteed to pay for drugs that go through FDA approval and target big conditions.

At the end of the day, the work you need to do to drive adoption of a technology is often very different from getting it to a certain performance level. The work priorities drive the type of organization you create. As much as we like to think that startups are about building technology, they are actually about selling technology and adopting it for specific markets so that they can do that better. 

### Related
- [[§Academia Constraints]]
- [[§Startup Constraints]]
- [[§Corporate R&D Constraints]]
- [[§Philanthropy and Non-Profit Constraints]]
- [[Systems research requires a lot of work that is expensive and uninteresting]]

[^1]: [[Kaizen is Toyotas process for continuous business process improvement]] 



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28 Aug 2021

24 Aug 2021

25 Aug 2021

26 Aug 2021

27 Aug 2021

About these notes

# Top of mind
(Updated 26 Nov 2022) 

The intention of this page is to serve the function of  [a /now page](http://nownownow.com/about)  - shamelessly stealing the idea of a notes-based now page from the indomitable Andy Matuschak.

Most of my time is spent building the new [new institution](spec.tech) I outlined [in the piece I wrote on building a private ARPA-riff.](https://benjaminreinhardt.com/parpa). (More details soon!)

This entails:
- Finding [potential program managers](https://benjaminreinhardt.com/parpa#its_possible_to_find_people_who_are_both_willing_and_capable_to_be_program_managers) and working with them on the fist steps of [[§Program Design]].
- Working on our first two programs. Specifically [[Nanomodular Electronics]] and [[Molecular Additive Manufacturing core]]
- Finding the money to fund these things. 

The nature of trying to create the organization still raises a number of research-y questions: [[Top of Mind Questions]]

And of course, I can’t help occasionally durdling around with some other ideas:
- [[In Defense of Academia]]
- [[Research is not a commodity]]
- [[We have no good metrics for successful research]]
- [[Directly acknowledging alignment and misalignment can be powerful]]
- [[Periodic table of research organizations OR Innovation Engineering]]
- Recording an occasional [Idea Machines Podcast](ideamachinespodcast.com)as a way to talk to fascinating people and share it with the world.

On the physical front, I’m pretty plateaued on climbing :-/ I’m not quite sure how to break through it — probably need to get a coach. 



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Benʼs working notes

§Corporate R&D Constraints

Related

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