ARPA-E did a poor job riffing on DARPA

# ARPA-E faces different industry incentives than DARPA
Unlike DARPA, ARPA-E does not create technology that the government wants to buy. 

This ‘three party system’ creates many problems.  There is already an energy market with large entrenched players. This is not so different from a military - people build their careers on specific warfare paradigms and spend a ton of money on equipment that could be obsoleted by new paradigms. So where is the difference? Some possibilities include: DARPA is more connected into the military than ARPA-E is into the energy companies ([[Manage the Transfer not the Technology]]) so it’s easier to find champions. 

Sometimes [[DARPA works on programs that go against the established paradigm]] but sometimes they work on things that the military ‘customer’ wants. Speculatively, this balance between working on weird shit and desired things may make the weird shit more likely to go through? The military, while they are driven by fair prices, are not driven by profit so they don’t make tradeoffs the same way the energy market does. The energy industry won’t make market precommitments for fusion power because they can’t know how to price them because it depends on what the price of oil and gas will be in the future.

The commercialization issues facing ARPA-E need to be considered for any riff on the [[§ARPA model]]. [[If something needs to be integrated into preexisting systems or manufactured, a pipeline into those systems is important]]! 

ARPA-E could get around this in one of two ways: being hyper aggressive in trying to create entirely new industries (decentralized power?) or establishing pipelines into the existing industries. Instead of technology, one of the most powerful things ARPA-E might be able to do is to establish something analogous to FDA trials. 

### Related
- [[Commercializing DARPA COVID research case study]]
- [[Advanced Market Commitment Thread]]



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DARPA does multiple levels of top-down problem generation and bottom-up solution generation

§ARPA model

DARPA PMs use seedling programs to ‘acid test’ the riskiest pieces of a program idea

The DARPA execution framework boils down to showing that thing is not impossible, showing that thing is possible, and then making that thing possible

Program Managers need to be entrepreneurs

Workshops could be a way to start small building a private ARPA

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

Maverick work is lonely and DARPA provides peers and community

DARPA underutilizes the internet to execute on its model

There are many tight feedback loops built into the ARPA model

Why would people want to work with PARPA?

fuchsRethinkingRoleState2010

How do you have skin in the game around ideas?

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

DARPA provides a derisking role for people in other organizations

How to think about the differences between FROs and private ARPA programs

Focused network building is a valuable early step for riffing on ARPA

weaverNSNotesOfficer1946

PARPA needs to be obsessed with PMs

How could the ARPA Model take advantage of the internet?

Wide vs deep networks and Clusters with respect to DARPA

DARPA is a coordination mechanism

The ARPA model can lead to inbreeding

What do online DARPA workshops look like?

Matthew Feldman Conversation 30 Apr 2020

Q2 2021

# A large part of a DARPA program manager’s job is focused network building
 [[fuchsRethinkingRoleState2010]] points out that DARPA program managers act as [‘brokers’ and ‘boundary spanners’ - terms of art for people who connect relatively unconnected clusters of people](https://funginstitute.berkeley.edu/wp-content/uploads/2012/10/Brokerage-2C-Boundary-Spanning-2C-and-Leadership-in-Open-Innovation-Communities.pdf). DARPA PMs network in the literal sense of creating networks, not just plugging into them.  

The idea of *focused* networking is important. DARPA program managers have a clear purpose for building the network - first to generate a clear target in an area, then to solicit paths towards the target, and then to make sure that the people who are executing on those paths know about each other so small adjustments to the plan can happen as frictionlessly as possible.  It is easy to network in an indeterminate way, hoping that the connections you make will be useful one day. This indeterminate approach is how much people go about networking. 

[[“Networking” is just jargon for ‘building relationships with people who aren’t shoved in front of you by life’]] and PMs need to do this at every step of [[The DARPA execution framework boils down to showing that thing is not impossible, showing that thing is possible, and then making that thing possible]].  What does this tactically look like in practice?

In the first step of the process, PMs need to find and become friends with (ideally) everybody who is working on ideas adjacent to the area where they want to have an impact to get a sense of what’s possible. It’s important for PMs to come from the research world so that they already have many of these connections. *Realistically, PMs are able to get this access because they bring the possibility of funding.* PMs also bring these people together in small groups to dig into which possibilities are not impossible and what it would take to make them possible. If the PM is doing their job well the people at the `workshops` won’t already know each other and will continue to poke at ideas together on their own.   The second step of the process leverages the network built during the first step to quickly do seedling programs. [[DARPA PMs use seedling programs to ‘acid test’ the riskiest pieces of a program idea]]. 

During the third step of the process, PMs depend heavily on the network to send unique solutions their way. The PMs host `performer days`  - small private conference for all the people working on different pieces of the program. These conferences force people to talk about the work they’re doing *while* they’re doing it, which while uncomfortable, enables people to share solutions to tricky problems and form more connections. You could imagine this leading to problematic correlation between results because everybody would want to copy whatever the shiniest group is doing but at this point all the performers are locked in to whatever approach they’re trying.

You could think of DARPA PMs as manually playing the role of a [[Designed Serendipity]] system. Both connecting the right people at the right time and giving them an incentive to help each other out. 

[[waldropDreamMachine2018]] talks about how [[JCR Licklider]] was always flying around to different university groups.  In addition to bringing together all the people who thought they were crazy to be interested in human-focused computing, he also helped create several new lab groups.  Today, [[A new professor needs about $1.5m to get going]]. Interestingly, I haven’t seen any organizations devoted to seeding labs in a structured way beyond funding new professors who would have worked on those ideas anyway.

PMs also build networks of people in different classes of organizations - government, academia, startups, and large companies. Connections between different verticals is one way that [[The definition of the ARPA model has changed over time]] -  in 1960’s, large companies had much more academic R&D arms and startups were barely a thing. In this way, DARPA PMs are acting to [[Manage the Transfer not the Technology]].  This shift in the PM role is important. Two reasons that we don’t have more awesome sci fi shit ([[§How do we get more awesome sci-fi shit?]]) that I’ve seen over and over are 1) technology that needs actual manufacturing or high capital investment isn’t able to jump the gap from a lab to a startup prototype or from a startup prototype to a real scalably manufactured product and 2) there are many pieces of technology that are amazing but don’t warrant an entire VC backed startup on their own. It seems like building connections between different organization classes while the technology is being built could address both problems. Worth digging into more. 

In a way, the Program Manager acts like a product manager in a tech company, talking to the customer and modeling their needs. For DARPA the military is the customer but the difference is that the ‘product’ isn’t something that can be purchased (TRL 9) but something that is proved out enough that other branches of the military.

### Related
- [[DARPA facilitates idea sex both between PMs and Performers]]
- [[Definiteness means that you have a long-term plan]]
- [[The ARPA model may be able to utilize the internet for awesome]]
- [[DARPA underutilizes the internet to execute on its model]]



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§Academia Constraints

Notes and statements should be bloody

Academics are incentivized to do peer-reviewed publishable work

Is peer review a good idea - paper

DARPA facilitates idea sex both between PMs and Performers

# Academic credit is a first-to-the post system
Everybody in academia pays attention to ‘who was the first person to discover this thing.’ The way that it counts is by publishing something about it.  

The first-to-the-post system and the existence of preprint servers means encourages people to create a lot of false positives. This would not be a problem if there was a good way of alerting the world that something was a false positive but there is no good system for that. 

The first-to-the-post system makes everybody obsessed with being “scooped” because the first person to publish an idea gets the credit even if someone comes along afterwards and explains it better. Of course, it doesn’t always happen like this and you also get into fights where there is a better known expression of an idea and the person who originally came up with it spends an inordinate amount of time yelling that they came up with it *cough* Schmidthuber *cough* 

This is why [[The word ‘novel’ is used as an idea bludgeon in academia]] - extending an existing idea is not in high esteem even if it’s an important reframing that can unlock something important. 

The first-to-the-post system doesn’t encourage dispersing a concept beyond making sure everybody knows that you came up with it and will cite you as such. ([[Dispersion is the process of a heuretic impacting the world]]) But [[Just having the piece of literature doesn’t solve the problem]]. 

### Related
- [[weinsteinBretWeinsteinPrediction]]
-  [[Manage the Transfer not the Technology]]



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Roadmapping could be thought of as backwards-chaining together a series of conception-only heuretics

gertnerIdeaFactoryBell2012

Just having the piece of literature doesn’t solve the problem

The creation of a heuretic has two steps - conception and implementation

Systemic decay theory of stagnation

Conception is the imaginative design of a heuretic

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

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

We can cleanly attribute philosophical ideas to people but we cannot do the same with science or technology

# Academic research is often just the concept half of creating a heuretic
Is there a mapping between concept and implementation halves of heuretics and TRLs? [[Conception lives in the realm of TRL 1-3]]

Since [[Academia is the game where you gain status by getting attention for new knowledge]] the work necessary to make the knowledge impactful is devalued.  Those priorities are not necessarily ‘bad’ but it does require explicit acknowledgement that there is a lot more work necessary to make a full heuretic. [[The creation of a heuretic has two steps - conception and implementation]].  The focus on conception means that *academia does not create complete heuretics.* Incomplete heuretics make transferring the technology out of academia deceptively hard ([[Manage the Transfer not the Technology]].) Taking over an incomplete heuretic requires a lot more context.  [[Context is important and underrated for knowledge transfer]]. 

The need for context may suggest that it’s important to have whomever is taking over involved in the process *during the conception.* Organizations that successfully do transfers seem to do this:
- [[TandemLaunch]] talks to people in the lab before
-  [[Calvin Nguyen]]’s entrepreneur-in-lab idea is trying to embed the person who would do the transfer 
- Most successful companies that spin out of labs either have someone who did the research as a cofounder 
	- Are there counterexamples to this? [[Are there examples of successful technology transfer out of a lab without close collaboration between someone in the lab and the external organization?]]

A corollary to the increased need for context around a concept-only heuretic is that copying an incomplete heuretic is much harder than copying a complete one. Throwing things over the wall doesn’t work unless you have clean interfaces and [[only complete heuretics have clean interfaces]] and therefore the conception half of a heuretic can’t be thrown over a wall. This absence of clean interfaces may be a big reason why [[Just having the piece of literature doesn’t solve the problem]]! 🤯

Another corollary is that chaining together conception-only-heuretics will *absolutely require* talking to people in order to get enough context and that it *can’t* be done automatically by reading papers. ([[Roadmapping could be thought of as backwards-chaining together a series of conception-only heuretics]]) 

### Related
- [[Technology Readiness and the Valley of Death]] - specifically the “ilities" 



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§Corporate R+D Constraints

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

# 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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Healthy corporate R+D requires a trifecta of conditions

In the past, corporate labs filled a particular niche in the innovation ecosystem

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

The niche corporate labs once occupied still needs to be filled

ICT Distillation

An Institute for Creative Technology - Riffing on ARPA

ICT Outline

# Corporate labs no longer fill the niche they did in the early-mid twentieth century
It’s fairly uncontroversial that  [[In the past, corporate labs filled a particular niche in the innovation ecosystem]]. There’s a strong sense that as of 2020, they no long fill that niche. However, corporate labs (including [[Xerox PARC]] and [[Bell Labs]]) still exist, so we need to create a strong argument that they no longer fill an important niche. 

I’ll proceed by both looking at outputs and inputs. Outputs are just the labs’ real-world outcomes and note that corporate labs are no longer producing the sorts of outputs they once did. However, that argument has a causality problem - it could be that labs are actually filling the same niche and a confounder is causing decreased output. This confounder could be the same thing driving the [[Low Hanging fruit theory of stagnation]].  So we also need to outline the conditions/inputs that were arguably necessary for the labs to fill their niche in the past and illustrate how those conditions no longer exist. Neither approach is bulletproof but hopefully a combination of the two is convincing. 

A couple of caveats: there are many exceptions. [[AI research is an exception to the decline of corporate R+D]] and there are many potentially amazing projects happening at corporate labs around the world. One might argue that the exceptions *are* the rule - that there are just fewer technologies that would benefit from healthy corporate labs (remember, the Low Hanging fruit confounder.) It’s impossible to prove definitively, but evidence suggests that [[The niche corporate labs once occupied still needs to be filled]]. 

Going through [the characteristics of the niche](bear://x-callback-url/open-note?id=1CEDC5D7-9F94-4857-B69D-F2281EF30F61-88049-0002A16B9E377530): 
- Most corporate labs no longer work on general-purpose technology. The trend, as embodied by the [[Microsoft Research Founding Memo]]  is towards focusing on product development. This trend is also seen at  [[Google X]] where they pride themselves on killing ideas quickly and then focusing on a specific product. The trend towards product development pushes corporate labs more towards the same niche filled by startups. 
- Corporate labs no longer enable smooth transitions between [[Technology readiness level - TRL]]s. Either they are incredibly separate from the main org that they have no advantage over a startup or are so tightly integrated that anything new is quashed. In the first case, there is no systematic way for projects to move from the lab to the company, instead needing to go through the same ‘finding a champion’ and bespoke uphill battle rigamarole that any product going to market faces. have similar barriers to integrating into the main company as a startup. These barriers create the same [[The (idea) valley of death]] that the academia -> startup path faces. [[Manage the Transfer not the Technology]] In the second case, labs are so joined at the hip to the main org that they can’t work on anything besides incremental improvements. [[Separate soldiers and scientists]]. 
- Echoing the previous point, corporate labs often no longer work on potentially game-changing technologies with tight feedback loops. It feels like they’re either doing “show-off” work that leads to nifty demos and papers or grungy incremental work. To posit a reason why - technology improvement is no longer an existential imperative for most companies. They both have the option to acquire new technology through M&A and [[Companies want R+D to be off of their balance sheets]] and they face very different pressures from AT&T. [[AT+T benefitted significantly from creating better technology]]. [[aroraChangingNatureCorporate2015]] digs into this trend more. 
- Corporate labs no longer provide a first-class alternative to academia. With the exception of the big AI labs and maybe IBM research on superconductors, there are few areas where working at a corporate lab is as academically prestigious as being a professor. The reason is tied into how cutting-edge science and technology are no longer an existential concern for companies, which gives them little reason to hire and keep the best and most ambitious researchers, creating a downward spiral where there are no A players and the labs become susceptible to the fact that [[A players hire A players and B players hire C players]]. Additionally, the incentive shift to focus more on product development creates a cultural shift away from participating in the scientific enterprise. See [[The decline of science in corporate R&D]].
- Corporate labs no longer enable continuous work on 5+ year timescales. There are many commentaries on corporate short-termism that I won’t dig into. In short, a combination of focusing on quarterly earnings, competition and shareholders pushing companies to stick to their knitting, and the availability of M&A opportunities is not conducive to 5-10 year speculative projects. And not just just in the obvious sense that they might get cancelled. The more nefarious condition is that there’s no level of planning for how that project eventually becomes part of the core company so even if it’s not cancelled it can putter along forever.
- In most cases, corporate labs no longer probe deeply into natural phenomena and try to exploit them. As noted earlier, corporate labs just don’t do as much *science.* This isn’t to say that corporate labs ever did science for its own sake - but in the past they perceived a direct line between scientific discoveries and the health of the company. I suspect that this perception *is accurate* in most cases. The move away from Pasteur’s quadrant could simply be because we’ve exhausted exploitable phenomena and everything left is either too big, too small, or too obscure to make a difference at human time-and-size scales. Alternately, the move could be chalked up to a change in the nature of the companies and their products to be more specialized and less dependent on exploiting new phenomena. 
- 
### Related
- [[aroraChangingStructureAmerican2020]]
- [[Healthy corporate R+D requires a trifecta of conditions]]



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Exploratory Program Organization

Decoupling from market discipline is like cave diving

Manufacturing is important and often ignored

How does money work in PARPA?

Playbook for being a field strategist

bonvillianARPAEDARPAApplying2011a

Transition Manager job description

Metascience Workshop May 2022

The DARPA Model for Transformative Technologies

DARPA has three mechanisms for transferring technology

Possible FRO Structures

DARPA works where markets dont

Commercializing DARPA COVID research case study

Things about ARPA that can be improved

Ways an ARPA-riff could improve on Things about DARPA that can be improved

PARPAs third evolutionary stage is a full private ARPA

# DARPA has a mixed record on transitioning
Abstractly, the ARPA model is designed with the explicit intention that technologies get out of the lab and into the real world. Clearly DARPA has successfully transitioned paradigm-changing technology to the government, large companies, and startups, so the model isn’t a complete failure at transition. However, many DARPA technologies still fall into the [[The (idea) valley of death]] and even successful transitions often are not smooth.

DARPA’s history pockmarked with stories that go “and then at the end of the program, the military or industry said ‘that’s cool, but we like the way we do it now.’” Or perhaps worse, they say ‘that looks great’ but completely stop working on scaling up the technology after taking ownership. In some of these cases the outside organizations come around: DARPA funded the development of UAVs in the 80’s, the Navy took on and then killed the program ::TODO needs citation::, DARPA continued development until the military paradigm shifted in the 90’s. The story is similar for optoelectronics. While counterfactuals are hard, these common ‘near death’ stories suggest that there are many programs that vaporized on impact with existing paradigms.   

 [[bonvillianARPAEDARPAApplying2011a]]  cites some of theDARPA commercialization failures:
	- High definition display program
	- Thinking Machine - Connection machine parallel processing computer
	- Gallium Arsenide computer chips

Each transition failure, like unhappy families, is unique. Some of the failure modes do rhyme, though:
- The technology is great, but it doesn’t slot well into a product line at a big company and venture capitalists can’t see how it could lead to a massively valued company in less than a decade.
- The people who worked on the technology are great researchers but terrible entrepreneurs. The skills that make you good at seeking+executing on government projects can be quite disjoint from the skills that make you good at building and selling a product. This mode can apply both at the level of performers and program managers. 
- The pieces of the program don’t come together in one place. Each project in a program is a piece of a puzzle and most of the time they’re far more valuable together than on their own. For maximum impact those pieces eventually need a single home after the program ends. Personal computing was such a success story in part because as ARPA took its foot off of the gas pedal, [[Richard Taylor]] was able to bring the pieces in-house at PARC.  Speculatively, bringing the pieces together may have become harder because the Bayh-Dole act enabled universities to extract licensing fees on research they created with government money, potentially bleeding dry anybody who wants to license from multiple universities who each want their cut.   [[The relationship of government, academia, and technology fundamentally changed in 1980 with the Bayh-Dole act]] 
	- The idea of ‘prime contractors’ gets a bad rap but that is actually DARPA’s attempt to address this problem. **ALL THE PIECES NEED A HOME EVENTUALLY** which is this bizarre tension around externalized benefits [[R+D orgs lie on a spectrum between externalized and internalized execution and benefits]]
-  DARPA isn’t a full vertical technology machine (as opposed to something like [[Bell Labs]].) DARPA doesn’t actually own the technology so they can’t commercialize anything themselves.    
-  [[DARPA works where markets dont]] - markets defined broadly to encompass military acquisition, (if there were market forces driving a technology forward, why would DARPA need to work on it?) A program can fail to address the core reason why there’s no market. In a way, DARPA programs are like cave dives - you want to get somewhere that requires you to decouple from safety/the market but you need to end up at somewhere with oxygen/demand eventually. [[Decoupling from market discipline is like cave diving]]. You can miscalculate where you need to end up or the target can move. 
- Often, [[DARPA works on programs that go against the established paradigm]] and changing paradigms might endanger someone’s job or just be more work than any advocate is willing to do. 
- Often, the program doesn’t think about what’s going to happen beyond the end of the program until it’s several years in. As a result, design choices that could have made the transition more smooth have been “baked out.” 
- At the end of the day DAPRA (and most other ARPAs) are government entities. That means they have a limited set of legal “institutional moves” they can use to transition technology. 

Many of these failure modes illustrate the tension between ‘building something people want’ and ‘building something capable of shifting paradigms.’ 

[[Transitions are an area where an organization riffing on DARPA may be able to make big improvements on the original]]. It is important to first acknowledge that [[DARPA doesn’t exist in a vacuum]]. The mechanisms at its disposal are subject to the constraints of the world around it. In the 1970’s that meant large corporations like Xerox, defense contractors, or government labs. The constraints on those organizations have shifted, and VC-backed startups have become an important mechanism as well. DARPA has worked to adjust to this new environment - going so far as to create a commercialization team in 2017::TODO reference::, but from personal experience the adjustment has been clunky.  

### Related
- [[Commercializing DARPA COVID research case study]]
- [[DARPA has three mechanisms for transferring technology]]
- [[Manage the Transfer not the Technology]]
- [[Project management  is important if transitioning between TRLs is complex and expensive]]
- [[Parney Albright conversation 17 Jan 2023]]



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The linear innovation pipeline is more harmful than useful

Innovation requires multiple heuretics

One person or class of people do not deserve credit for an innovation

Would it be correct to use the term lab for innovation organization

# Innovation requires multiple people 
# Innovation requires multiple people 
[[Innovation is measured by impact]]. For a heuretic to have a large impact someone needs to conceive it, implement it, and disperse it.  Each of these often requires creating multiple support heuretics [[Innovation requires multiple heuretics]] and multiple skillsets. It’s rare that a single person has all of the skillsets you need to do (or manage other people to do) each of those pieces. 

Even more extreme, people with the skills in one stage of the process might not even *conceive* of the other stages.  The scientist who discovers a phenomenon in a lab might not realize that it could or should be made into a practical thing that lends itself to a particular business model or even that she *should* be thinking about those things. The brilliant salesman might not even realize that he should be hanging out with scientists and inventors. 

Each person needs context in order to do a good job with their role 

Something something handoff 

Argument for why an innovation ecosystem is relevant [[From the outside the innovation ecosystem looks complete and healthy]]

This may be another argument for [[There are a number of experiments that seem like they can only be done in the context of an organization]]

Of course, this also stands in contrast to the fact that [[New ideas need to come out of a single mind]]! 

### Examples
- Penecillin  
	- Alexander Fleming discovered the phenomenon in 1928
	- `Fleming was a famously poor communicator and orator, which meant his findings were not initially given much attention. [[27]](https://en.wikipedia.org/wiki/Penicillin#cite_note-Lax2004-27)  He was unable to convince a chemist to help him extract and stabilize the antibacterial compound found in the broth filtrate.`
	- Howard Florey figured out how to use it for medical applications in 1941
	-   [Jasper H. Kane](https://en.wikipedia.org/wiki/Jasper_H._Kane)  and G. Raymond Rettew used   [Margaret Hutchinson Rousseau](https://en.wikipedia.org/wiki/Margaret_Hutchinson_Rousseau) ’s deep tank fermentation techniques to produce penicillin at scale - otherwise the entire US supply was enough for one person
	- The scaling up was pushed by the US government

Software is an outlier because it actually enables one person to do all the steps of the process. Mark Zuckerberg could conceive, implement, and disperse Facebook.  

An upshot of this is that  [[One person or class of people do not deserve credit for an innovation]]

### Related
- [[Most tech transfer offices are not profitable]]
- [[Manage the Transfer not the Technology]]
- [[One person or class of people do not deserve credit for an innovation]]
- [[We stand on the shoulders of too many giants to give them all credit]]



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bahcallLoonshotsHowNurture2019

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

DARPA has a mixed record on transitioning

The definition of the ARPA model has changed over time

Therapeutics have established sales channels

Academic research is often just the concept half of creating a heuretic

Old school Pharma VCs add value because they help startups go through an established sales process to pharma companies

Academic credit is a first-to-the post system

Value capture does not always need to happen through IP

Principles for Innovation Orgs

The unrecognised simplicities of effective action number 2 - ‘Systems engineering’ and ‘systems management’ — ideas from the Apollo programme for a ‘systems politics’

Innovation requires multiple people

Technology transfer is a mystifying abstraction

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

Transitions are an area where an organization riffing on DARPA may be able to make big improvements on the original

Visions of ARPA

Asymmetric career risk impacts corporate R+D

PARPAs second evolutionary stage is an architecting institute

ARPA-E faces different industry incentives than DARPA

When a research org can't hand something off it seems to get stuck

# Manage the Transfer not the Technology
## References
- [[bahcallLoonshotsHowNurture2019]]


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Materials and manufacturing underpin civilization

Prototyping needs manufacturing in the room

Physical experiments are expensive

Implementation is the actualization of the heuretic in the real world

olsonManagingModesEffort2020

Bell labs brought many disciplines together under the same roof and broke down barriers between them

Parney Albright conversation 17 Jan 2023

New manufacturing paradigms are more likely than other technologies to generate positive second-order effects

Smart people no longer do base-layer work

Jeremy Glick conversation 18 May 2022

Tying between technology and the underpinnings of civilization

# Manufacturing is important and often ignored
Physical things need to be manufactured if you want to have more than one of them. There is a whole set of properties that make something more or less manufacturable. 

[[Skunkworks podcast]]  mentioned several times how the key to moving fast was to have people who were going to manufacture the thing sitting in the room to point out where a design might be manufacturable. 

Things can fail in manufacturing in a bajillion different ways. You could build a completely functional prototype of something that is entirely unmanufacturable.   (Can things fail in scaling in a bajillion different ways?) 

The people who handle and have heuristics around manufacturing are usually not the same people who do the prototyping and original building. The informal interaction that ‘having the person in the room’ during the prototyping process enables is a [[Transaction costs]] that is reduced if the people are all part of the same firm. (Thanks [[Robert Coase]]!) The whole point of design reviews is when the people who are manufacturing and designing are not in the same firm (or not in the same org within the same firm.) 

The gap between people who design and prototype and people who manufacture is especially bad in academia. ([[§Academia Constraints]].) From my experience, people doing university research literally do not think about manufacturability at all. This missing perspective is one of the gaps left by [[aroraChangingStructureAmerican2020]]. 

Manufacturing is the atomland instantiation of software scalability in bitland. ([[§Atomland, Bitland, and Peopleland]]  - what is the people land version? Replicability? Systemization?) The difference is that you don’t have to worry about scalability until you increase use by orders of magnitude, manufacturing needs to happen from day 1 for dispersion. Manufacturing is the gap between [[TRL7 - prototype version in real environment]] and [[TRL8 - production version tested in real environment]]. A “production” version has been manufactured in the same way that every other version would be.

 In software when going from TRL7 to TRL8 you can get away with doing almost nothing - maybe changing a few more settings, adding more tests, etc. There are two reasons for this tiny gap. The first reasons is that “Production” versions of software don’t need to scale because the cost to disperse a new production version that does scale later is nothing. The only cost is the up-front cost of building it in the first place. The other reason is that you can develop and prototype using roughly the same ‘tools’ as you would use to produce it thanks to standardized software environments, containerization, etc. In Atomland there are gaps both between the CAD/simulation design, the prototype, and the manufactured product that can be uncrossable if you didn’t take into account where it the thing needs to go. 

In the past, manufacturability was less of an issue because the gap between prototyping tools and manufacturing tools was smaller. Everything was made by hand or with simple tools anyway. 

Manufacturing is an important place where [[Learning by doing]] is key. 

Manufacturing is a failure point in DARPA’s transitions. [[DARPA has a mixed record on transitioning]].  From what I can tell DARPA doesn’t really consider manufacturability during programs. 

Constraints on manufacturing:
- Tooling
- Time
- Materials
- Accessibility
- Number of parts

### Related
- [[List of things that affect dispersion]]
- [[Manage the Transfer not the Technology]]
- [[Skunkworks podcast]]
- [[Process Knowledge]]
- [[Dan Wang 2019 Letter]]
- [[Technology transfer is a mystifying abstraction]]
- [[New manufacturing paradigms can remove bottlenecks for other paradigms]]
- [[Materials and manufacturing underpin civilization]]



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Science Business - Book

Large companies use startups as outsourced R+D

# Old school Pharma VCs add value because they help startups go through an established sales process to pharma companies
Organizations like [[Deep Science Ventures]] have relationships with Pharma companies and the FDA that enable their startups know exactly what milestones they need to hit to move from one phase of the FDA process to another. These milestones are coupled to [[Development Tranches and Tranched Funding]] from the large pharma companies and strong assurances that the startup will be acquired when certain milestones are met. This process basically reduces market and channel risk to zero so all the risk is concentrated in tech. It is rare for a company to have both market risks be eliminated so it enables the pharma VCs to take large % of equity.

The relationships flow through the VC firm because the firm will do this hundreds(?) of times, while any individual founder will go through the process twice if they’re really good just because of the timescales involved in the full cycle. It’s very similar to how certain banks help companies IPO. 

It makes you wonder whether  [[An ARPA-Riff could establish sales channels for frontier tech]] 

DA Wallach does a good description of the relationship between Pharma and startups here on this podcast: [D.A. Wallach – Investing in Healthcare - Invest Like the Best, EP.166 — Invest Like the Best — Overcast](https://overcast.fm/+Lzu0aeyz8/39:45)



### Possible list of examples
- Arch Venture Partners
- Vivo Capital
- F-Prime Capital
- Sofinnova Ventures
- NEA
- Atlas Ventures
- Venrock
- Canaan Partners

### Related
- [[Manage the Transfer not the Technology]]



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Innovation orgs need to be aligned with their money factory

§PARPA

Can you use transparency to earn enough trust to be opaque?

What should be in the Packet

# Principles for Innovation Orgs
As with any set of principles, these are flexible. However, the more you bend them, the more poorly an innovation org will work. 
- Opacity is important
	-  [[Opacity is important to DARPA’s outlier success]]
	- The more people who can influence a plan before it happens, the more likely it is to be derailed. 
		- Note that there should be an explicit distinction between purely advisory roles, direct power roles, and delayed power roles
	- Opacity is important because [[Making a game more fair reduces its variance]].
- [[Demos are like afterburners]]
	- Demos are essentially promises: we will create the thing this demo evokes in your imagination
	- The purpose of a demo is to rally excitement and resources that can get you to reality
	- Demos are *not* a sign of progress in and of themselves - often they can actually take work away from progress on the actual thing because in addition to the TRL ~3 system, you need to build a lot of instrumentation to make it useable that will be thrown away and replaced in the final output
	- In an ideal situation you wouldn’t need demos - the money factory would trust the innovation org enough to give them the resources they need without the demo and understand technical reports enough to understand the progress that has been made
	- Ideally demos would follow the “apple model” where the demo is just showing off the thing that is already in production
	- [[We should strive to unbundle Elon Musks Brain]]
- The ability to change direction quickly is tantamount
	- Anything that makes it harder to change direction should be eliminated. Among other things this includes unchangeable roadmaps and metrics, overhead and restrictions on spending money and hiring, and generally needing to ask permission before taking action. 
	-  [[Make living documents]] can enable quick direction changes while keeping stakeholders in the loop. 
- More money fungibility is better
	- Research is never linear.  Like [[The Idea Maze]], you might think beforehand that you need to zag when you actually need to zig. In the context of innovation orgs, budgets with restricted line items - so much for personnel, so much for equipment, etc. - can severely hamper an organizations ability to execute on 
	- [[It is relatively easy for DARPA PMs to re-deploy funding]]
- Long-term thinking is structural
	- You can say all you want about long term thinking but if you need to constantly go and raise the next quarter’s worth of money, you will be thinking about what you can do in order to raise that money
- It’s all about people 
	- There are two ways that people make or break an innovation org. 
	- First, like other high-variance activities all the results are driven by the tails of the distribution. This power law means that the best person for the job is going to be *massively* more impactful than the second best person and could make the difference between success and failure. As a consequence, innovation orgs with restrictions on who they can work with - whether because of salary caps, credential requirements, or personality tests - are likely shooting themselves in the foot.
	- Second, the importance of people mean that incentives on those people are critical. If individual incentives aren’t aligned with the organization as a whole, chances of achieving your desired outcome decrease significantly. 
- [[Manage the Transfer not the Technology]]
	- Shamelessly stolen from the excellent book [[bahcallLoonshotsHowNurture2019]] 
	- In the same way that the world will not beat a path to your door if you build a better mousetrap, if an innovation organization does some amazing work, that work will not automatically impact the world. It requires work from people outside the organization to diffuse it, whether through commercialization, continued public development or other mechanisms. It takes planning and work to make sure this will happen, and too often people focus too much on the organization itself and less on how the organization will hand off the work it has done. Often this involves collaboration *before* the work is done.
	- The other part of this principle is that once you have the right people and incentives in place, further external management and intervention generally hurts the innovation org. 
- Explicitly call out misalignments between the money factory and the innovation org
	- At the end of the day, the goal of the innovation org is to create things that have never existed before and the goal of its money factory is something else (otherwise it would be an innovation organization itself.) It is uncomfortable, but if you can explicitly call out the misalignments between these two goals, you can come up with a strategy to mitigate the effect of this  misalignment.  There are several ways to mitigate the misalignment. You can constrain what the innovation org works on - this works when the money factory itself has an extremely broad mandate, like DARPA and the DoD or Bell Labs and AT&T. [[Alignment between people playing different games can happen on finite time scales]] so another approach is to explicitly define the expectations and timescales that each party expects *before* starting. 
- Research is not linear
	- You can’t expect regular progress reports from an innovation organization. Einstein published four of is five most impactful papers in a single year, and the fifth paper ten years later. 
- Trust matters
	- Innovation orgs are by their very nature trying to do things that have never been done before. You can’t do something that has never been done before by acting in entirely normal ways (otherwise it would have been done!) So in order for an innovation org to work, stakeholders both outside and inside need to be comfortable with actions that are hard to justify or predict a priori. That is, innovation orgs will not work without a level of trust.  



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We only see the science based public stuff from the middle of the century because the engineering was classified

# Technology transfer is a mystifying abstraction
Even in containerized software, which is basically the most modular and legible thing in the world, there is a ton of friction handing a project from one team to another team.  “Hell is other people’s code.” [[‘Off the shelf’ technology is code for TRL 9 and maybe 8]]. 

Technology transfer makes sense in a world where if you can look at a drawing on a piece of paper and know everything about it, which was the case until the late 19th century. 

We can abstractly think of you handing the “IP” - patent documentation to me but in order for me to actually do anything with it, so much more needs to be done. Patents aren’t even written by the people who created the technology anymore! Perhaps an extreme analogy but patents represent technology the way the dollar represents gold. The way in which a patent is insufficient to transfer a technology is analogous to how [[Just having the piece of literature doesn’t solve the problem]]

[[A patent describes the key new concept]] but usually doesn’t touch many implementation details. Since [[The creation of a heuretic has two steps - conception and implementation]], it means that patents actually underdetermine a heuretic. This underdetermination means that raw “IP” is useful for suing someone who successfully copies the idea (because to copy the whole of it they need to copy the new concept piece), but it is not particularly useful for copying the heuretic itself. See [[Dupont was unable to replicate viscose rayon]]. [[aroraChangingStructureAmerican2020]] cites this phenomenon as one of the drivers of the creation of corporate labs. The implementation piece is (I suspect) one reason why large companies acquire startups for their technology instead of just buying the technology itself. [[A technology company is a package of human capital and IP]]. 

### Related
- [[Heuretics come out of people’s brains (TECHNOLOGY IS PEOPLE)]]
- [[Manage the Transfer not the Technology]]
- [[Context is important and underrated for knowledge transfer]]
- [[Most tech transfer offices are not profitable]]
- [[Are there examples of successful technology transfer out of a lab without close collaboration between someone in the lab and the external organization?]]
- [[Any technology that comes out of a university needs to go through their tech transfer office]]



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Dispersion is a more appropriate word than dissemination or diffusion for heuretics

DARPAs director is important

The DARPA Model for Transformative Technologies identifies four different risks for technology to get to get to the market

# The DARPA Model for Transformative Technologies
This book is actually a series of papers that are better addressed separately. 

### DARPA’s Historical Mission and Organization
- Always had multiple thrusts
	- Originally missile defense, nuclear weapon detection, space
- AGILE considered to have run amok with little oversight ::but licklider also had little oversight::
- Early 70’s/late 60
- s trouble
- As of 2020
	- BTO
	- DSO
	- I2O
	- MTO
	- STO
	- TTO 
- TTO and STO are “systems offices” that produce “poc” engineering systems for DoD. [[TRL3 - Proof of Concept]] can have such different meanings. 
- TTO and STO primarily fund engineering teams but fund fundamental things when they run into problems
- Typically, a program will have specific technical objectives, a budget of tens of millions of dollars, and will last for three to five years. 
- DARPA itself does not build actual operational prototypes of new systems; it turns over “proof-of-concept” prototypes to other parts of the defense and commercial worlds—a process that DARPA calls “technology transition”. - 11
- The agency’s budget for 2019 is $3.427 billion a year. DARPA has no laboratories of its own. It is a funding agency.  - 11 [[DARPA is ~0.5% of the Department of Defense Budget and ~12% of that is basic research]]
- the objective is to create new possibilities and capabilities and particularly seek “change-state” technologies 
	- other times integrating existing technologies in new ways may significantly transform capabilities 
- it is “challenge-based”, and it is a “connected model” that connects scientific research to these technical challenges.10 
- Agency leaders expect their program managers to consult widely with relevant technical communities, test and retest their ideas, and constantly learn.  [[There are many tight feedback loops built into the ARPA model]]
- Because the agency has no laboratories or researchers that it must fund year in and year out, it has the freedom to move away from unsuccessful projects to focus on promising ones  [[It is relatively easy for DARPA PMs to re-deploy funding]]
- Independence is necessary
- the agency is free to hire whomever it thinks are the best people for specific projects 
- If a particular DARPA program is successful, then the agency may fund additional “generations” of three- to five-year programs in this technical area. 
	- This point about multi-generational investments is important and not always well understood  - it’s how they work on Long term things
- DARPA sometimes will fund work in additional technical areas relating to major new technology.  ::More sequioa style carrier group::
- IPA process allows darpa to hire people quickly despite government, other ways to get around government procurement requirements ::[[DARPA is incredibly flexible with who it hires to be program managers]]::
- Claims darpa helped start materials science …::Ask about this::
- DARPA is willing to challenge existing technologies and the organizations that produce and use them.  (From [[ARPA-E and DARPA - Paper]])
	- [[DARPA works on programs that go against the established paradigm]])
- DARPA is fortunate that the Defense Department can be both willing and able to turn new prototype technologies into actual products. 
	- This is often hard because DARPA by definition changes ways of doing things  [[DARPA works on programs that go against the established paradigm]]
	- [[Manage the Transfer not the Technology]]
	- [[DARPA has a mixed record on transitioning]]
- DARPA works directly with chip producers who want to commercialize things
	- ::but what about situations where there’s not a clear absorber::
	- [[DARPA has a mixed record on transitioning]]
- [[Lyndon Johnston]] did a good job of rewriting history around sputnik
	- [[Lyndon Johnston rewrote history around the American reaction to Sputnik]]
	- See [[Moonrise - Podcast]]
- Underlying this “high-risk—high-payoff” motif of DARPA is a set of operational and organizational characteristics including: relatively small size; a lean, non-bureaucratic structure; a focus on potentially change-state technologies; a highly flexible and adaptive research program. [[DARPA is relatively tiny and flat]].
- in contrast to the then existing Defense research environment, ARPA was designed to be manifestly different. It  did not have labs. It did not focus on existing military requirements. It was separate from any other operational or organizational elements. It was explicitly chartered to be different, so it could do fundamentally different things  ::There is something to the idea of doing something different just so you can do different things [[§How do we get more awesome sci-fi shit?]]::
- Components of risk [[The DARPA Model for Transformative Technologies identifies four different risks for technology to get to get to the market]]
	- Lack of knowledge around phenomenon or concept
	- Lack of knowledge about applications
	- Inability to gauge cost of arriving at answers to first two points
	- Difficulty of determining broader operation and cost impacts of adopting the concept
- DARPA things can fall into the valley of death when the researchers say it’s ready to hand over and the recipients say no [[DARPA has a mixed record on transitioning]]
- Over the years DARPA has made considerable effort to develop mechanisms to engage potential “customers” in an emerging concept.  ::Such as?::
- idea-driven and outcome-oriented 
	- Outcomes can be different! 
	- ::Ideas as opposed to what?::
- Different roles
	- Basic science -> technologies
	- Exploring disruptive capabilities
	- Developing tech strategy into defense strategy
	- Foster fundamental change in a domain
- But almost never are they (PMs) the main, let alone sole, investigator of the concept/approach.  
- One aspect of DARPA’s success has been efforts to define strategic challenges in detail  
	- [[More precise descriptions of technology could enable faster progress]]
	- [[It is important to precisely define problems in an area]]
- DARPA has been a pursuer of new breakthrough technologies *independent of defined needs* and that address needs but not requirements. - These two roles are in tension.
	- Demonstration prototype efforts can be resource [sumps](https://en.wikipedia.org/wiki/Sump)
	- [[DARPA works on programs that go against the established paradigm]]
- DARPA responsible for  “Revolution in Military Affairs” implemented in 1990’s - [[Was the shift from ARPA to DARPA a focus change or a process change?]]
	- Stealth
- DARPA leaders need to keep DARPA from being ‘toned down’ 
	- [[DARPAs director is important]]

3.  [[NSF and DARPA as Models for Research Funding - An Institutional Analysis]]
4.  [[The Connected Science Model for Innovation—The DARPA Model]]
5. [[The Value of Vision in Radical Technological Innovation]]
6. [[ARPA Does Windows - The Defense Underpinning of the PC Revolution]]
7. [[fuchsRethinkingRoleState2010]]
8. [[DARPA’s Process for Creating New Programs]]
9. [[Some Questions about the DARPA Model]]
10. [[DARPA - Enabling Technical Innovation]]
11. [[Program Management at DARPA - A Personal Perspective]]
12. [[Lessons from DARPA for Innovating in Defense Legacy Sectors]]
13. [[ARPA-E and DARPA - Applying the DARPA Model to Energy Innovation]]
14. [[IARPA - A modified DARPA Innovation Model]]
15. [[Does NIH need a DARPA?]]

### Published
2020
## Questions
- What would it take to get a successful professor to establish a non university lab?
- What theoretical paradigms would completely destroy current huge business models?
- In a way DARPA acts like [[Old school Pharma VCs add value because they help startups go through an established sales process to pharma companies]]
## Thoughts
- Things that change the ways of doing things are both disruptive and related to the dispersion rate of technology
- Chip producers are kind of like pharma companies
- Risk has to do with chance of not achieving outcome
- Vision as institutional memory
- Workshops are like ::concept-a-thons::
- What if non profit part of [[§PARPA]] really was pass through science funding - like “oh this lab needs a machine, we’ll just buy them that machine” 
- What you really want to do is create a copy of BSO, IPTO, DSO
- [[PARPA needs some sort of connection to people who can support paradigm shifts]]
- Singapore could become a testbed nation
- The more homogenous an industry is, the harder it is to enter - fewer niches
- 
## Related
### Cites
-   [[The Imagineers of War]]
-  [[Evolution of U.S. Government Innovation Organization: From the Pipeline Model, to the Connected Model, to the Problem of ‘Political Design]]
- [[The Computer as a Communications Device]]
- [[Man-Computer Symbiosis]]
## Authors
- [[Bill Bonvillian]]
- [[Richard Van Atta]]
- [[Patrick Windham]]
## Link
[The DARPA Model for Transformative Technologies: Perspectives on the U.S. Defense Advanced Research Projects Agency - Open Book Publishers](https://www.openbookpublishers.com/product/1079)



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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

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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Defining clear timescales and scopes for collaborations is a tool to minimize politics

# The unrecognised simplicities of effective action number 2 - ‘Systems engineering’ and ‘systems management’ — ideas from the Apollo programme for a ‘systems politics’
`‘There isn’t one novel thought in all of how Berkshire [Hathaway] is run. It’s all about … exploiting unrecognized simplicities… Warren [Buffett] and I aren’t prodigies. We can’t play chess blindfolded or be concert pianists. But the results are prodigious, because we have a temperamental advantage that more than compensates for a lack of IQ points.’` -- Charlie Munger, Warren Buffett’s partner.`

Paper has two goals:
1. How to make government effective
2. How to develop a focused project

Also how to build a cheap training program 

- [[George mueller]] used new techniques to manage complex systems and make Apollo successful

- The core problem keeping government projects from being effective is a mismatch between political institutions and the scale and speed of change in the world

Government is a combination of:
1. Centralization of power in unremarkable people
2. A powerful bureaucracy that prioritizes generating rules over solving problems
3. No incentives to solve deep problems
4. Media and intellectuals don’t understand how to effectively solve problems either

Political system has no self-repair mechanisms (as opposed to science and markets

Failure looks like
- groupthink, 
- confirmation bias, 
- management chaos as events move faster than internal OODA loops,

Comparison to start of WWI:
- Things move 10^3x fasters
- 10^6 more destrictive power
- Potentially able to kill 10^10 more people

Technology passed a complexity threshold after WWII
People invested Systems Engineering and Systems Management which were successful in some domains

- Taylor invents scientific management p4
- Tizzard uses operations research (OR) to integrate radar into British during WWII (P5)
- Bush resisted OR (why?)

Arguably 30’s and 40’s laid groundwork for modern fiends
- Information Theory
- Cybernetics
- What is Life?

Von Neumann ripping apart Samuelson (p6)
Von Neumann Weather Control
(i) The General and Logical Theory of Automata (delivered 1948, published 1951). 
(ii) Five lectures on Theory and Organization of Complicated Automata (delivered in 1951).
 (iii) The Theory of Automata, a manuscript written 1952–3 but unpublished until after his death.
 (iv) The Computer and the Brain, drafts for the Sulliman lectures supposed to be delivered to Yale in spring 1956, written 1955 – 6, but unpublished until 1958. 

At the heart of all this was a fundamental problem: the inadequacies of conventional mathematics for making predictions about complex non-linear system (p8)

In [[Can we Survive Technology? - Essay]] Von Neumann argues that we can’t stop accelerating progress and it puts more and more tools of destruction into our hands. New political systems to exercise judgement and control these problems .

Von Neumann realized hydrogen bombs would get small enough to fit on a missile. Convinced [[Bernie Schriever]] that US needed IBM program.

ICBMs required a level of management  of complexity that had not been seen before

Changed structure away from Prime contractor structure

Beaurocracies have lots of people who can say ‘no’ but can’t say ‘yes’

According to Johnson, this was a first for the Air Force ‘where the project manager had both technical and budgetary authority’ as previously every project drew funds from several budgets and required separate processes for making decisions. 

*Concurrency* - pursuing several options in parallel

Schedules were standardised across all the different players and coordinated centrally but in such a way that managers could access them and see quickly the status of the project. 

‘Black Saturdays’ were monthly days on which the whole project was reviewed and 7 responsibility for all problems assigned to individuals. 

‘Give me the bad news. I can take it. I will not fire you for giving me the bad news. I will fire you if you don’t give me the bad news’, Schriever said (echoing Warren Buffett: gimme the bad news, the good news can wait) [[Good managers ask for the bad news first]]

- ‘Matrix management’ allowed organisations to manage projects using people spread across different functional departments all reporting to a project manager as well as their department head. 
- ‘Configuration control’ and ‘configuration management’ connected changes to specifications, designs, hardware, and operational and testing procedures within an overall system for scheduling. 
-
[[Jay Forrester]] ran [[WHIRLWIND]] - which is somehow related to [[SAGE]]

Almost all proponents of Systems engineering were connected to Caltech or MIT. JPL did good systems management. 

McNamara came to power and imposed new beaurocratic requirements on R&D and shifted to phased (serial) instead of concurrent (parallel) planning ::why - cost?::
`Overall the Pentagon became more powerful and consciously emphasised cost issues over performance issues: state-of-the-art performance was less of a priority. `
![](BearImages/C764BA86-E19C-4DDC-B781-BACAAE9CCEBE-68756-00033546911674F8/7F4E2DDF-C59E-40B0-B2AA-9D663179ED31.png)

`Simon Ramo, co-founder of TRW, defined systems engineering as: ‘the design of the whole as distinct from the design of the parts. Systems engineering is inherently interdisciplinary because its function is to integrate the specialized separate pieces of a complex of apparatus and people — the system — into a harmonious ensemble that optimally achieves the desired end.’ He described a ‘systems engineer’ as ‘a peculiar form of generalist [with] the faculty of understanding enough of each of the pieces and [is] good at communications.’ `

Systems management is a lot about *interfaces* ::Related to  [[Manage the Transfer not the Technology]], [[Creating legible interfaces is expensive]]::

Only PMs could approve changes to interfaces 
Von Braun did not like Mueller’s approach ::Why?::
ELDO was a big flop because it had terrible communication, people had incentives to overrun costs
`Missile reliability increased from about 50% in the early 1950s to 85-95%. The total engineering development effort for the the Atlas/Titan Project required about 31 million engineering man-years and took just over four years from 1955-9. In contrast, the B-52 bomber project required about 7 million engineering man-hours and took eight years. `

`Tragically, after the success in 1969 ambitions were curtailed, funding was slashed, and NASA itself forgot many of the lessons of Apollo’s success. When the extreme focus and discipline brought by Mueller was relaxed, NASA slipped back to technical failure, repeated accidents, deaths, and wasteful budgets. It lost institutional memory and the culture that made it a success, as the reports on various disasters showed. ` ::This seems very handwavy::
Mueller attributed this decay to the fact that systems management was forced on NASA and people didn’t understand *why* things weree the way they were.
`‘like most such structures, unless you have people that really understand why these things were done that way and what needs to be accomplished, it tends to drift into being form but losing its functionality’ (Mueller)`
`developing the solar system for commerce and science could give humanity a joint endeavour that increases fundamental knowledge and demands new forms of international cooperation helping to suppress natural tendencies towards traditional international relations which will be fatal in the long run.`::Why would this turn out differently than literally every other international collaborative effort::
even the rare organisations that do brilliantly on a hard problem struggle to maintain their culture once vital people leave
`other very unusual and very successful people like Charlie Munger — a sort of ‘no bullshit’, steely focus on the job without concern for the normal things that divert people. Such people are probably +3 standard deviations (~1:1,000) on a handful of crucial dimensions (IQ, will power, management ability, metacognition etc) so the combination is very rare and, because of society’s incentives, many of these people will not be working in a niche that optimises their rare talents. (How many +4SD intellects — global population ~250,000 — are also willing and able to navigate politics and bureaucracies as successfully as von Neumann?) `

`The flip side of this problem is the opposite problem most clearly represented by the hubris of McNamara’s Whiz Kids and the Vietnam disaster: treating systems analysis and associated tools as capable of eliminating the fundamental issues of conflict such as complexity, the fog of war, passion and so on. `

Systems engineering doesn’t work on people - either on other groups int he government or on complex social systems

Core principles:
- Organization-wide orientation: everybody understanding as much as possible about goals and plans
- Interfaces between parts are key
- Extreme transparency and communication ::How does this square with too much noise and the fact that some things should be brought up in private first::
	- ::Maybe a policy of “re directly at least once before re all::
- Configuration management - huge effort into the initial design of a complex system and then a process for changes in a disciplined way that involves everybody.
	- ::This requires people to prioritize speed and communication, otherwise the need to get everybody onboard grinds everything to a stop::
	- ::Lots of effort on initial design flies against move fast and break things philosophy::
- Orgs that have coped well with complexity have spent money on physical and informational structures to reinforce open communication
- Long term budgets
- Complex mix of centralization and decentralization. Vision, goals and strategy from top dow. Decentralized operations for speed. 
- Extreme focus on errors - black Saturdays and meetings focused on making problems clear. 
- Spending on redundancy to improve resilience. 
	- Most people treat redundancy as waste.
- When important knowledge Is discovered it needs to be codified and systematized so it can be easily learned and used by others
- Process and documentation is good but fluid and purposeful - process for ass covering is bad.
	- `When asked how he would do a similar programme to Apollo now (1990s) Mueller responded that the only way to do it would be as a classified ‘black’ project to escape the law on issues like procurement. `
- Saving time saves money. 
- People and ideas > technology
- 

### Published
## Questions
- What was the complexity threshold passed after WWII?
- Are spacecraft complex or just complicated?
- Can you measure complexity?
- Why was controlling weather harder than Von Neumann predicted?
	- Chaos theory?
- How many engineering man-hours do projects take now
- 
## Thoughts
## Related
- [[Program and project management is important and valuable]]
### Cites
- Can we survive technology?, von Neumann, 1955.
-  The Apollo Tradition: An Object Lesson for the Management of Large-Scale Technological Endeavors, Seamans and Ordway (NASA administrators), 1977. 
- The development of ballistic missiles in the US Air Force, Neufeld, 1990. Inside NASA, McCurdy, 1993. 
- Secret of Apollo, Johnson. Systems, experts, and computers, edited by Hughes. Doing the Impossible, Slotkin, 2012. 
- A fiery peace in a Cold War, Sheehan, 2009. 
- Rescuing Prometheus, Hughes. 
-  Team of Teams, Stanley McChrystal.
[^1]:
## Authors
- [[Dominic Cummings]]
## Link



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Frontier tech has bespoke sales channels

Software has established sales channels

There is a tradeoff between an organizational culture that is good at addressing market uncertainty and one that is good at addressing technological uncertainty

There are secretly two kinds of biotech

Xora has two possible strategies for leveraging Singapore biotech

Xora shouldn’t focus on therapeutics and diagnostics

It is unlikely for things to come into Healthtech from Left Field because both creating and investing in it is fairly obvious

# Therapeutics have established sales channels
The FDA approval process coupled with [[Development Tranches and Tranched Funding]] provide new therapeutics with a relatively straightforward (but still risky!) path from lab to market. This process depends on the incredibly low market risk for therapeutics that make it through the FDA — insurance companies buy the majority of therapeutics and will often agree that they will buy and how much they will pay for a given therapeutic assuming it makes it through the approval process. 

This process is mediated in large part through the investors. ([[Old school Pharma VCs add value because they help startups go through an established sales process to pharma companies]])

This is an example of a systematic transfer between a lab and a market. ([[Manage the Transfer not the Technology]])

By contrast,  [[Frontier tech has bespoke sales channels]]. 



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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

Many DARPA Programs are multigenerational

Better value capture mechanisms

Profit is important for both practical and ideological reasons

Value capture is for funders

Omniscience is not enough

# Value capture does not always need to happen through IP
The classic non-IP way to capture value is a trade secret. Trade secrets only work if you can ensure that the idea remains secret. So trade secrets aren’t an option for research coming out of labs where people are incentivized to publish. It’s also an unstable equilibrium because someone else can patent it and then stop you from using your own thing.  Trade secrets at the end of the day are another way to create a monopoly. 

[[Mick Stadler writes a letter in 1976 on effective transfer mechanisms]] brings up another possibility that actually depends on as many as possible using a piece of research. If you issue a non-exclusive license or no license at all, the inventors can capture a lot of value by consulting with companies who are using the thing. 

Why don’t people do more non-exclusive licensing? I could make up some reasons: the value from an exclusive license can be exponentially larger than a non-exclusive license with several licensees. It’s more work to create many deals around a non exclusive license than a single deal around an exclusive license. Companies want new technology to give themselves an edge over competition and a non-exclusive license doesn’t do that. 

What other options are there?

### Related
- [[It is hard to capture value from research]]
- [[It is hard for a private ARPA to capture value without hamstringing impact]]
- [[A technology’s value is relative to the amount of value it unlocks]]
- [[Is it possible that there are just different scales that value capture can happen on?]]
- [[Manage the Transfer not the Technology]]
- [[Context is important and underrated for knowledge transfer]]
- [[Current value capture mechanisms are crude]]



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Government procurement and prizes are the same thing

# Visions of ARPA
- Hoover I
- States that ARPA became DARPA in 1996 
`Pushing the Frontiers of Basic Science to solve a well defined use-inspired need`

### Policy Exchange Recommendations
1. Government should move rapidly to establish UK ARPA
2. Government should clearly set out role, objectives, and mission of ARPA
3. Government should get bipartisan support for ARPA
4. ARPA should not execute research or have its own labs
5. ARPA should focus on developing technologies on a 10-15 year horizon
6. ARPA should be judged on successes not rate of failure
	- Ruthlessly cut off funds for failing projects
7. Small number of missions
	- 2-4 missions
	- Reviewed by minister of science every five years
8. Project managers must be capable, empowered, and given genuine authority
9. Minimal Bureaucracy
	- Project managers should need to convince 2 people
10. Government must also develop commercial pull-through to bring ARPA tech to market
	- Reform of public sector procurement
	- Single strategic customer for an area
	- Establishment for tech to market group
- Structure
	- Offices (2-4)
		- Projects (5-10 per office) 
			- Grant Allocations ￡500k-5m per grant
### A Brief History of the UK Science and Innovation System
- Department for Scientific and Industrial Research established in 1916
- DSIR didn’t raise level of industrial research in UK 
	- [[What can be learned from government and research policy in the United Kingdom, 1914-1965]]
- Medical research council formed in 1920
- Agricultural research council formed in 1931
- “Loss” of penicillin - it was discovered in the UK but commercialized in the US
- National Research Development Corporation in ~1950 to commercialize discoveries from publically funded ideas to 'prevent foreigners from filching ideas’
- Minister for science 1959
- Ministry of Supply (which handled purchase of military equipment) and Ministry of Aviation controlled several laboratories. 
- Ministry of Technology formed in 1964 
	- Took over Atomic Energy Authority, NRDC and parts of DSR
	- Dismantled in 1970
- Lord Rothschild pushed customer/contractor principle
- Labour returned to office and set up National Enterprise Board in 1974
	- Created Inmos semiconductor and Celltech biotech
- Thatcher in 1979 sold off Inmos, and Celltech
- NEB+NRDC -> private British Technology Groups - got first refusal rights on discoveries coming from publically funded research
	- NRDC failed to patent monoclonal antibodies
- Thatcher approach was to use public money only for basic research
- Research Assessment Exercise/Research Excellence Framework created to make sure research directed towards areas where "peer review established research as outstanding”
- Policies of 1960’s/1970’s “failed”
- 1997 tried to figure out how to create more university-private linkages (p16)
- 2013 government moved more towards helping tech get from lab to marketplace (p17)
- Put Research Councils and Innovate UK into a new org - UK Research and Innovation (UKRI) (p18)
- UKRI focus on
	- Healthcare and Medicine
	- Robotics and AI
	- Batteries
	- Self driving vehciles
	- Manufacturing and materials 
	- Satellites and Space
- Unclear whether support for this system will continue

### Lessons from US Science and Innovation System
- 1969 R&D in Germany+Japan+UK+France = $11.3B
- 1969 R&D in US = $25.6B
- US has porous boundaries between universities and industry
- More antimonopoly in US
	- Cites IBM software unbundling
- Funds to VC increased after 1979 when government pension funds could invest
- NASDAQ was more suited to young companies
- Uncoordinated government agencies help fund in US
- DARPA initial funding for networking had no obvious commercial payoff (p22)
- Trump administration wanted to dismantle ARPA-E
- Balance between freedom and accountability - as more freedom is given to individuals, the talent of the individuals matters more.

### The government’s proposals
- “new funding agency broadly modeled on ARPA”
- ￡800 over five years
Less formally
- Offer academics funding >10 years 
- Attributeds idea to [[Dominic Cummings]]

### A summary of the DARPA Model
- Cites [[Science: The Endless Frontier]] as creating the ‘Pipeline Model' for science
- Technology push/technology supply model
- [[The (idea) valley of death]] between ‘front end’ - university/R&D etc and 'back end' - usually private.
- Describes DARPA as an intermediary 
- Says DARPA passes off research
- DARPA uses a Technology visioning process
- Looks for revolutionary breakthroughs 
- Principles From 39. DARPA (2005), ‘DARPA - Bridging the Gap, Powered by Ideas,’ DARPA: Arlington, Va. ; DARPA (2003), ‘DARPA Over the Year (p29)
	- Small and flexible 100 program managers and office directors
	- Flat 
	- Entrepreneurial 
		- Program managers are there only 3-5 years
	- No Labs
	- Focus on impact not risk
	- Seed and Scale - funding to a bunch of different things 
	- Autonomy from civil service hiring process
	- Hybrid model - taps firms and universities
	- Teams and networks - tries to create great groups
	- Acceptance of failure
	- Orientation to revolutionary breakthroughs in a connected approach
- None of those principles are written down
- Catalyze formation of great groups - focus on people as innovators and connect them
- Apply the Island/Bridge Model - put innovation entity on an island and protect from 'suits'
	- 	Cites [[Lockheed Skunkworks]], [[Xerox PARC]], and [[IBM PC Project]]
	- Skunkworks and PC Project had strong bridges, PARC didn’t
	- DAPRA uses connections to Secretary of defense as bridge
- Create a community of people both inside the organization and outside of it that creates legacy of thought
	- Cites [[J.C.R. Licklider]]
- Connecting Technologists and operators 
	- DARPA TTO stays in constant contact with DoD to make sure inventions are relevant
- Transitioning technology
	- DARPA can build directly into the DoD procurement process
	- Claims DARPA computer tech was originally civilian and supported by VC
	- Claims VC is not working for “hard tech” that requires more manufacturing or 
	- ARPA-E created ‘tech to market’ group
		- Tech to market group tried things like ‘creating alliances with established companies' and 'follow on funding
- IARPA had >70% of projects that passed midpoint be adopted by an intelligence agency

### ARPA: A critical addition to our innovation landscape
- ARPA should aid transition for ideas from lab to market
- Network of ‘Catapults’ working closely with industry
- ‘Technology pull’ comes from big companies
- Reasons industry hasn’t picked up research from universities
Communication related
	- Industry tends to focus on higher TRLs and engages less with science
	- Academics are not motivated or rewarded for having ideas applied
	- People who think broadly about where research could go are not rewarded in the current systems
Risk, Customer, and Industrial Landscapre related
	- Hard to find funding for high risk ideas with significant capital costs to demonstration
	- UK doesn’t have supply chain in some areas like materials
	- No market for some ideas
- - Henry Royce Institute is UK’s national institute for advanced materials
- ARPA doesn’t need to stimulate good research
- ARPA needs to ‘pick winners and take risks’ 
- Things an ARPA will need
	- Great program managers
	- Few constraints
	- Be able to risk public money
	- Concept of a customer
	- Universities that can deal with tranches of funding that can stop
- Places an ARPA might be effective
	- Graphene
	- Photonic materials
	- Photon multiplying materials 
(Energy trnaisiton, clean water, sensing and monitoring)
- “Crown shaped molecules to sieve CO2 out of the air”

### UK ARPA: An experiment in science policy? 
#### Author
- [[Richard Jones]]
- Science funding is based on one of three philosophies
	1. Competitive funding for projects - groups propose work to be done and is judged by other scientists
	2. Funding for people
	3. Funding for program managers/leaders
- ARPA customer shouldn’t be academia or industry
- Contract R&D labs like [[SRI International]]don’t exist in the UK
- ARPA should not be part of UKRI

### ARPA’s place in the UK’s Public Research Landscape
- ARPA should be part of UKRI

### Navigating high scientific risk and high innovation risk to reap the rewards of ARPA funding
- Getting science funding is inefficient 
- Funding bodies don’t want to take on risk
- Claims ARPA-E has been less successful with commercialization
- Areas that should be funded: clean growth, AI, healthy aging, future mobility
- Could provide funding to early researchers who want to work on crazy things
- Public procurement is too afraid to take risks
- Focus on more local procurement

### Designing a British DARPA
- Estimate that 85% of DARPA projects don’t work
- Lots of stuff 

### When failure is the key to success
- Need to make the org risk tolerant 
- Kill projects that aren’t doing well and ones that aren’t going fast enough
- Mission is important
- Key pieces - focus, long-term backing, high failure tolerance, sufficient funding
- Where does the customer come from?
- Mandate that 5% of public sector budgets need to be spent on innovative solutions
- Public procurement as an innovation driver

## Questions
- Do people actually know what they’re saying about commercialization or is it a [[Suitcase Handle Word]]
- [[How successful did DARPA look ten years in?]]
- How much of the structure that we analyze and praise about DARPA was actually in place during the time period when DARPA created the outputs that we praise?
- Why is it necessarily true that markets like energy take a long time? Didn't electrificatin happen privately and 
- Does Royce institute have a list of companies that want advanced materials and which ones they want?
- Would researchers accept grants with “no go” gates?
- How much funding do you *actually* need
- What if companies would spend more on new solutions
## Thoughts
- Factually incorrect that ARPA became DARPA in 1996
- Could you measure burocracy by the average number of people you need to convince/number of people who need to look at something to get permission for something?
- The review of British policy never talks about WHAT WORKED
- General tech areas are bullshit
- Uncoordinated government agencies leading to good research is related to [[More Uncorrelated Mistakes lead to more success]] 
- There is something valuable in an organizations desire for self-preservation (contra in the government how it can just be killed at will)
- They say that DARPA is an intermediary bridging valley of death but it's really not
- Maybe should use revolutionary breakthroughs instead of [[Quantum Leaps, Innovations, and Discrete Improvements]]
- Island/Bridge Model seems to be the same as [[Separate soldiers and scientists]]+ [[Manage the Transfer not the Technology]]
- Bridges need to be back to senior people in the production org
	- This person needs to be actually involved in the bridging not generally handwaving
		- This model is how old school pharma companies work
- The concept of “Ideas stuck in lab” is lazy thinking. The reason ideas are "stuck in labs" is because they weren't created to ever leave the lab. 
- So fucking little conviction about the things that should be funded without the balls to say "leave it up the porgram managers"
- [[“Failure is good” is bullshit]]

## Related
## Authors
Editors
- [[Ian Mansfield]]
- [[Geoffrey Owen]]
- [[William Bonvillian]]
- [[Julia King]]
- [[Richard Jones]]
- [[Jo Johnson]]
## Link
https://policyexchange.org.uk/wp-content/uploads/Visions-of-Arpa.pdf

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Benʼs working notes

Manage the Transfer not the Technology

References

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