How does money work in PARPA?

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

“The System” of AT&T

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

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

Problems that require system-level solutions often require massive teams

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

The boundaries between industry government and academia are fuzzy

Why try to emulate DARPA and not Bell Labs

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

An Institute for Creative Technology - Riffing on ARPA

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

# §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]] 
* [[Corporate 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]] 
* [[Expensive research needs to address an existential threat eventually at an organizational level 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]]
* [[Healthy corporate R+D requires a trifecta of conditions]]
* 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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§Academia Constraints

Decoupling from market discipline is like cave diving

Current value capture mechanisms are crude

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

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

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

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

PARPA could walk the line between profit and non profit

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

Startups and Academia are bad at addressing structural constraints

The Architected Innov􏰂ation Institute - Engineering Scientific Revolutions

# §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?

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

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

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

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

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

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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# 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 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]], 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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Healthy corporate R+D requires a trifecta of conditions

Having your life on the line forces you to contend with objective reality

Peter van Hardenberg Conversation 20 Nov 2020

# Expensive research needs to address an existential threat eventually at an organizational level to maintain support
[[Alignment requires existential threats]] and [[Innovation orgs need to be aligned with their money factory]] so the conclusion that R+D efforts need to address an existential risk to maintain support follows almost by definition. 

#### Distinction between the normative discussion and the practical discussion
This note is going to focus on the realities of how innovation organizations continue to get money in the door. It’s easy for the discussion to go down the track of “Painkillers vs vitamins! If I’d asked people what they wanted, they would have said ‘a faster horse.’ Make things people want! Is physics worthwhile if it never becomes a product?” These are questions of “what should a research organization do?” Which is important and related to “what should a research organization do *to stay alive?*” For the former, see: [[‘Solving problems people don’t even know they have’ is the epicycle of ‘problem-based’ framework of tech development]]. 

#### Refining the statement
The original title of this note was “R+D orgs need to address existential threats to maintain support.” That statement appeals to a sense of simplicity and there are plenty of examples of research orgs that lost support because they didn’t really address an existential threat ([[Dynamicland]] and BP Venture Research come to mind.) However it’s also not true: there is a large class of R+D orgs that are supported but continue to limp along ineffectively (Bell Labs still exists!) So perhaps “Effective R+D orgs need to address existential threats” is more accurate. This would explain the difference between ARPA-E and DARPA, why Bell Labs declined once it no longer staved off regulators, etc. 

However! There are plenty of examples of effective research orgs that do not address existential threats. In fact, most great scientists were not actually out to address existential threats - Galileo, Newton,  Rutherford, Einstein, etc. They just managed to cobble together enough money from patrons or side hustles to keep going. Patreon-sponsored contemporaries are similar. The notable pattern is that these examples are all individuals or small groups. Aha! It suggests that perhaps there is a [[Nebulous]] threshold below which effective research can work off of ‘throw away’ money and above which people start looking at money spent on research as ‘buying’ something - we could call this point “expensive.” 

There’s also the niggle that a lot of the work that makes us outsiders perceive a research organization as effective is some of the *least* existential-problem-addressing that the org does: transistors at Bell Labs, interactive computing at DARPA, etc. However, at the same time Bell Labs was discovering better wire sheathings that saved AT&T billions of dollars and DARPA was working out ways to detect nuclear explosions anywhere in the world. This contrast can be resolved by thinking about addressing existential threats not at the project level but at the organization level. 

So, the correct statement is perhaps that *expensive* research needs to address an existential threat *eventually* *at an organizational level* to maintain support.  Perhaps this is excessively navel-gaze-y but I think it’s important to try to answer the question “why do so many research organizations suck and how do you build one that doesn’t?” 

#### Expensive
Clearly, what entails ‘expensive’ research varies wildly. However, I would suggest that contrary to common intuition it isn’t just proportional to how much money you have. There are many examples of massive organizations and billionaires balking at small number relative to their revenue (or GDP.) I could create a hand-wavy explanation involving cognitive biases, paper wealth, or something else. But suffice to say, what entails “expensive” has much more to do with the amount of control a funder has and whether they think they’re going to get their money back than with how much money they actually have.  For independent organizations that aren’t investments the ‘expensive’ threshold seems to be reasonable salaries for ~1-3 skilled people. Weirdly it also tends to be independent of the *number* of funders  - ‘inexpensive’ orgs funded by a single billionaire tend to have about the same threshold as those funded by hundreds of people on Patreon.[^1] Also keep in mind that this is referring to sustained funding for an organization - one-time project-based funding is another animal. 

#### Eventually
Most organizations have a ‘grace period’ to dick around. 

There’s also a question about the frequency of hits that an organization needs to maintain, which seems to be a function of how big those hits are and how existential the threats they address actually are. [[DARPA has a 5-10 percent program success rate]] which I suspect it can get away with because military superiority is *very important* to the US government, while most charities need to show progress every semi-annual fundraising season.  

#### At an organizational level
I feel like this piece is pretty clutch. The *organization* needs to address an existential threat.[^2] Effective research organizations seem to build a portfolio of projects that address existential threats at a sufficient rate to maintain the perception that they’re addressing existential threats. 

This portfolio approach is clutch for several reasons. There is uncertainty around any given project as to whether *it* will address existential threats and often you can’t honestly answer that question a-priori without doing some work or strangling the project in the cradle. See [[bahcallLoonshotsHowNurture2019]] and the concepts of “warty babies” and “the three deaths” for more about this. Additionally, the projects that directly address existential threats for funders and those that are most interesting or globally impactful are often disjoint. [[The work at Bell Labs that was most valuable to AT+T and the work that was most valuable to the world was fairly disjoint]]. In the same way that winners pay for duds in a VC portfolio, aligned work can cover for misaligned work if the alignment is considered at an organizational instead of a project level. 

If the organization produces aligned hits at a sufficient rate, it ideally can create a [[§Trusted Hierarchy]]  where the trust (and money) is flowing *down* the power ladder. Congress trusts the DARPA director who trusts the deputy director who trusts the PM. The PM doesn’t need to justify a program to congress, but instead to the deputy director. This is why [[Opacity is important to DARPA’s outlier success]] - it can work on crazy shit that would never get a-priori funding from congress, but it needs to earn the trust to maintain that opacity by delivering as an organization. 

Another way this manifests is through contract research organizations that fund themselves through contracts or grants, but use that money to fund internal research.  

### Related
- [[All the work at Bell Labs needed to tie back to the system]]
- [[One way for an R+D org to address existential threats is to tie into a core product]]

[^1]: This may have to do more with the people seeking the funding - people don’t feel comfortable with asking for more money than they need to live?
[^2]:Or at least be perceived to be addressing existential threats - there are many organizations that successfully maintain funding based purely on great marketing. It’s worth paying attention to why this marketing works but I’m going to otherwise ignore it because I am bad at pulling off false pretenses and would prefer if the world had fewer of them. 


                        

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Government research is corporate R&D for United States Inc

The niche corporate labs once occupied still needs to be filled

# Healthy corporate R+D requires a trifecta of conditions
A pattern starts to emerge when you start to ask “how are ‘golden age’ corporate R&D orgs” different than one today, which no longer seem to have the impressive output or cache that they once did? [[Corporate labs no longer fill the niche they did in the early-mid twentieth century]].

It seems that all of them have a trifecta of conditions
1. They were run by a monopoly 
2. They were working on a clearly high potential technology 
3.  That technology addressed one or more existential threats to the company[^1]

*Digging in*
1. **They were run by a monopoly**
More specifically, they were run by companies that were extracting monopoly rents on some high-margin product. Xerox was the only game in town for copying machines (which, believe it or not were core to many businesses), Dupont controlled well-known materials like Teflon or Lycra, Corning was Pyrex and Silicone, and of course AT&T controlled the telephone networks. 

2. **They were working on a clearly high potential technology** 
The technologies were the sort of thing where they had strong conviction that if they could actually pull it off technically, they would be able to sell it. Of course if you can replace a vacuum tube with something that uses 1000x power people would use it. Of course if you can create glass that won’t shatter when you heat it up and cool it down people will use it. 

What’s not clears why so many fewer technologies seem to fall under that umbrella now. It could be that we picked the low hanging fruit in atom land. [[Low Hanging fruit theory of stagnation]]. It could be that people are just more pessimistic about what we can build. It could be that timelines have shrunk, and there is no high-potential technology on those timelines. It could be that people think in terms of products instead of technology. Or it could be that companies have scoped down. 

3. **That technology addressed one or more existential threats to the company** 
1+2 are insufficient if the technology the lab works on doesn’t address existential threats to the company. This is important and ignored because there is a huge gap between an invention and widespread impact. Someone needs to put in active effort to diffuse technology into the world and if the company doesn’t feel intense pressure to do the diffusion work, technology will languish. The importance of diffusion is also why, while it’s not necessary, it really helps for the technology to be in line with the company’s core product capability. A technology that can take advantage of existing manufacturing and distribution channels is just more likely to be impactful.  [[Someone needs to buy manufactured technology eventually in a monetized economy]].

[[Xerox PARC]] is an illustrative example through the different dynamics of the laser printer, the personal computer, and the lab itself.  While PARC is legendary because of the impact of its personal computing work, structurally most of that impact shouldn’t have happened. PARC’s personal computing work *wasn’t* tied to Xerox’s core business. The only reason it was impactful was because of  a crazy set of contingencies - Steve Jobs’ ‘raiding party’ and Bill Gates quickly following suit. Xerox wasn’t set up to scale up and build a *business* around personal computing. [[Different technologies require different organizations]].

The laser printer, on the other hand was directly in line with Xerox’s core business - they already sold printers and copiers and this was just a better, faster version. 

Through this lens, AT&T almost cheated on this front because its core business was the full stack of “communications” so everything from chemistry that kept telephone poles from rotting to information theory were actually tied to the company’s core business. 

Transistors had the potential to integrate directly into AT&T’s system and enable faster, cheaper service. Faster, cheaper service was an existential priority for AT&T because if they didn’t continue to improve, the US government would have an excuse to break them up. [[Regulation drove many of Bell Labs’ actions]]. 

I suspect that the existential threat criterion in a big factor in the difference between [[Google Brain]] and [[Google X]].  Better machine learning technology directly ties into google cloud, search, etc. While drone delivery and autonomous cars can potentially be big businesses they are basically orthogonal to google’s core business. 

### Related
- [[Alignment requires existential threats]]
- [[Expensive research needs to address an existential threat eventually at an organizational level to maintain support]]
- [[Innovation orgs need to be aligned with their money factory]]

[^1]: Condition #3 was originally “1+2 are insufficient if the technology the lab works on isn’t tied into the company’s core business.” However, this isn’t quite right. There are situations where technology work on the core product can have nothing to do with existential risks to the business (for example Salesforce.) Similarly there are situations where technology work has little to do with the core product but addresses an existential threat (for example flashy research at Bell Labs keeping regulators from breaking up the monopoly.) 



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

# 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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Academics are incentivized to do peer-reviewed publishable work

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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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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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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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
* [[aroraChangingNatureCorporate2015]]
* [[aroraChangingStructureAmerican2020]]
* [[Is there R&D spending myopia?]]


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Benʼs working notes

§Corporate R+D Constraints

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