§PARPA

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

The distinction between areas, programs, and projects

Treating Science Fiction as case studies from a future history

Program design could be thought of as creating a causal graph of blockers and hypotheses about how to mitigate those blockers

Program design is a coordination tool

The value of planning is inversely proportional to the cost of experimenting

Program design is like creating the lego instruction book

Instead of planning not working as well as people in the past thought, as a society we’ve become worse at technological planning

How does money work in PARPA?

Physical experiments are expensive

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

How do you measure real progress in research?

What role does scenario planning have in technological program design?

Maps help you figure out possibilities

Top of mind

There was a high return to technological roadmapping compared to not roadmapping in the past

fuEnablingMissionImpact2021

Efficiency biases systems towards false negatives

An ontology around peoples attitudes competency and roles is important for program design

Explaining things like I’m five helps avoid the Einstellung Effect

The tech tree model suggests that explicitly drawing out tech trees is important

An ARPA-like program could be the first step towards making general-purpose telerobotics inevitable

Funding is path dependent and funding curves matter

A predictably successful idea is composed of known unknowns that that a team can execute against

The crux question of program design is when to commit

Planning to Start vs Planning to Finish

Say ‘X is more important than Y’ instead of ‘X is important’

What are the different kinds of bottlenecks?

zwickyNewMethodsThought1967

What does a modified PARPA Heilmeier catachism look like?

Betting markets for research ideas could circumvent expert review killing ideas

There are two kinds of program design conversations — expansive and focused

The role of a visionary is to bootstrap the new from the old

Is there a way that python collab notebooks could be integrated into program design and roadmapping?

Roadmapping is related to the ability to create leading metrics for success

What is PARPA’s edge?

Jim Matheson and Joshua Krieger conversation 14 Jul 2021

How do you think about the risk profile of PARPA programs?

Hunting vs Farming Ideas

Work backwards with questions and work forward with answers

Joe Rollin conversation 10 May 2021

Precisely listing entrenched beliefs could be an important part of bottleneck analysis

Abstractly you want a good way of visualizing a bunch of chained vectors

Block Diagrams need to be in easily editable format

Geoff Anders conversation 7 Jun 2021

Geoff Anders conversation 9 Jun 2021

Getting people to talk about speculative research extensions is hard

Josh Greenberg Conversation 18 May 2021

Program design as a recursive response to the Heilmeier catechism

Roadmaps should have statements like “if we gain the ability to do X then we should do Y”

What are the pieces of a roadmapping architecture?

# §Program Design
- [[What are the goals of the program design process?]]]::needs work::
- [[Program design is like creating the lego instruction book]]
- [[Program design could be thought of as creating a causal graph of blockers and hypotheses about how to mitigate those blockers]]
- [[The crux question of program design is when to commit]]::needs work::

### Hypotheses
- [[The first step of roadmapping is frontier mapping]]
- [[A roadmapping framework may make the ARPA model more effective]]
- [[Talking to experts can expose bad equilibria, the moves necessary to get out of them, and considerations for doing them like cost constraints]]
- [[You can create a framework for talking to people that makes it easier to generate a roadmap]]
- [[You could build tools to make program design more efficacious]] 

### What are roadmaps
- [[What are the properties of maps?]]
- [[Roadmaps have two pieces - the map and the path]]
-  [[Roadmaps are a theoretical construct to escape inadequate equilibria]]
- [[List of Roadmapping Examples]]
-  [[Most roadmaps suck]]
	- [[Most roadmaps are not execution oriented]]

### Why Roadmapping
- [[Instead of planning not working as well as people in the past thought, as a society we’ve become worse at technological planning]]
- [[Roadmapping could be thought of as backwards-chaining together a series of conception-only heuretics]]
- [[Roadmaps can enable faster progress than blind exploration]]
- [[Technological roadmapping can give an idea of where technological evolution can go and how to get there]]
- [[A roadmapping framework may make the ARPA model more effective]]
- [[Program design is like performance debugging a vision]]
And of course we need to look at the possibility that this is all BS
- [[The roadmapping process is heavily context dependent and there may not be a generalized roadmapping process at all]]

### Building better roadmaps
- [[Design for roadmapping system]]
-  [[Person targeted questions for roadmapping]]
- [[What are the eight steps of Wigmore analysis translated to science?]]
- [[Technical Planning Principles]]
- [[Good roadmaps work backwards from a goal]]
- [[Good Simulations could be the ‘why now’ of roadmapping tools]]

### Glossary
- [[The distinction between areas, programs, and projects]]
- Bottlenecks -> Roadmap -> Execution
- Language
	- Binding Constraints
	- Affordances
	- Dependencies 
	- Project
	- Program
	- Area
	- Causal graph of blockers
- Steps
	- Frontier Mapping
	- Finding the right people
	- Comparing different paths
	- Goal setting
	- Finding serious contexts of use
	- Different institutional ‘moves’
	- Respectability cascades
	- Spending curves

### References
- [[And Suddenly the Inventor Appeared]]
- [[Wigmore Guide - Paper]]
- [[vincentTradeoffCentralConcept2017]]
- [[zwickyNewMethodsThought1967]]



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Uncertainty always involves risk but risk does not always involve uncertainty

raoGUTSGrandUnified2018

Research has many orthogonal and non orthogonal classification axes

Risk needs a metric

People like funding winners

What does ‘working’ mean?

Narratives are how we deal with uncertainty

People have different and unknown caring money thresholds

Many knowledge generating activities are inherently illegbile

Anyone on the knowledge frontier is a bit of a crackpot

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

Institutions either make things more legible or act as trust brokers

Heilmeier — Licklider — Braben — Elon quadrants of Research Management

Innovation orgs need a money factory

Physics risk vs. Engineering Risk

We need to disentangle technology from tech

Productive Uncertainty

It takes a long time to explain research

To make new things you need to take risks

A taxonomy of research organizations and activities

There is some connection between the rate of feedback loops in activity design and the uncertainty associated with the critical path of the activity

Podcast List

Astro Teller conversation 1 Feb 2022

When you do not have a critical path you need to throw the idea of efficiency out the window

Knightian Uncertainty occurs when you hit the limits of theory

Glossary

Derisking is actually deuncertaintying

Kanjun Qiu conversation 18 Apr 2021

People often use uncertainty when they mean unpredictability

Research projects have a halting problem

# Knightian Uncertainty
`”Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated…. The essential fact is that ‘risk’ means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomena depending on which of the two is really present and operating…. It will appear that a measurable uncertainty, or ‘risk’ proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all.”`
- [[Risk, Uncertainty, and Profit]]

`“There is a fundamental distinction between the reward for taking a known risk and that for assuming a risk whose value itself is not known. A known risk is easily converted into an effective certainty, while true uncertainty, not susceptible to measurement.”` (Paraphrased by [Explained: Knightian uncertainty | MIT News | Massachusetts Institute of Technology](https://news.mit.edu/2010/explained-knightian-0602)) 

The inability to predict, even probabilistically, what will happen

Knightian uncertainty is anchored on the idea of imperfect knowledge about outcomes in situations where it is hard to measure those outcomes. 

A corollary is that [[Uncertainty always involves risk but risk does not always involve uncertainty]].

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

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Projects need to build narratives of perceived progress on top of pieces of evidence

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

The two ways to enable action is through legibility and trust

What are the different flavors of uncertainty?

People like to fund projects

Orthogonal projects destroy narratives

Evidence either implicitly or explicitly involves a critical path

Semon Rezchikov conversation 26 Dec 2021

# Narratives are how we deal with uncertainty
People are constantly acting in the face of deep uncertainty. Arguably *most* of our actions look more like [[Knightian Uncertainty]] than situations with a finite number of outcomes that could, or do, have probabilities associated with them. If we needed to do an expected value or risk calculation for everything we would be utterly paralyzed. This does, in fact, happen to people — I’ve been overwhelmed to the point of inaction with the multiplicatively cascading possibilities for what I could do and the resulting potential outcomes. At this very moment I could be writing this, or I could be sending an email, or I could be sipping coffee, or lying on the floor with my eyes closed, or going for a walk, or calling a friend… 

One could imagine navigating this uncertainty like Twoface from Batman, flipping a coin at every decision point. You could also imagine finding the best approximation to a risk curve for everything you do and doing a back-of-the-envelope expected value calculation. Most of us don’t do either of these things, and yet we manage to act through the uncertainty pretty well. 

Instead, we act based on the stories we tell ourselves.  “This is the most important thing because X,” “I’m doing this because …” “I have no choice but to …” Heuristics are narratives — “X is true enough that it’s worth acting on.” *We act through uncertainty by telling stories that collapse the massive decision-space to either measurable risks or a raw narrative.* 

Accepting that most people are acting off of narratives most of the time might seem like a rejection of logic and rationality. You might be tempted to say “well we *ought* to strive to act more from evidence and logic.” or to throw your hands in the air and say “so the only thing we can do is to make shit up?” The former is the path to self deception — if you actually accept that real uncertainty exists, by definition you can’t create risk curves and therefore acting based on evidence and logic is fooling yourself. The latter is overly nihilistic. 

I’m belaboring this point because I suspect that if we acknowledge that narrative is the only mental tool we have to act through uncertainty, we might be able to improve it. *There may be more powerful ways of constructing narratives.* For example, in the uncertainty-fraught case of creating new technology, you might ask: [[What role does scenario planning have in technological program design?]]

### Related
- [[Philanthropies are forced to show consistent short term progress]] because they need to do constant narrative maintenance 
- [[Historical narrative is not scientific inquiry or engineering design but it is valuable]]
- Perhaps this is just an extension to the distinction between [[Systems Thinking and Story Thinking]]
- [[Science fiction is a powerful tool for transmitting science or economic ideas]]
- [[Good science fiction can enable more awesome sci-fi shit]]

### References
- [[linStoryLieStory2020]]

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ICT Outline

# Top of Mind Questions
- Are there better ways to  deal with [[Knightian Uncertainty]]?
	- [[Narratives are how we deal with uncertainty]]
- [[What does a modified PARPA Heilmeier catachism look like?]]
- [[When should an idea that smells like research be a startup?]]
- - How doe we do better [[§Program Design]] and specifically fleshing out the idea that [[PARPA creates causal graphs of technology blockers, precise hypotheses about how to mitigate those blockers and then executes on those hypotheses]]

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

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28 Aug 2021

24 Aug 2021

25 Aug 2021

26 Aug 2021

27 Aug 2021

About these notes

# Top of mind
(Updated 19 Jul 2021) 

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

Most of my time is spent executing on the plan to build a[new institution](https://benjaminreinhardt.com/parpa-2-pager.pdf) I outlined [in the piece I wrote on building a private ARPA-riff.](https://benjaminreinhardt.com/parpa). This entails:
- Finding [potential program managers](https://benjaminreinhardt.com/parpa#its_possible_to_find_people_who_are_both_willing_and_capable_to_be_program_managers) and working with them on the fist steps of [[§Program Design]].
- Simultaneously working on designing a few programs myself. Specifically [general purpose telerobotics](https://benjaminreinhardt.com/parpa#general_purpose_telerobotics) and first steps towards an [artificial ribosome.](https://benjaminreinhardt.com/parpa#positional_chemistry)
- Working out the nitty-gritties of the [legal structure](https://benjaminreinhardt.com/parpa#legal_structure_is_critical_to_institutional_capacity) and getting the entities incorporated. 
- Finding the money to fund these things. 
- Reputationally bootstrapping the whole thing: the actual work becomes easier the more seriously people take you, but in order for people to take you seriously you need to do good work. 

If you want to see PARPA succeed, here are [[Ways to Help PARPA]].

More abstractly,  a thesis I’ve been playing around with as PARPA’s ‘theme’: [[Materials and manufacturing underpin civilization]] and the corollaries  [[What processes can be drastically changed with new manufacturing tools and capabilities?]], [[New materials can radically change technological constraints and enable previously unthought of applications]]

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

And of course, I can’t help occasionally dicking around with
- Creating a [[“This would be amazing BUT” list]] — would love more suggestions!
- Expanding on the idea of [[The linear innovation pipeline is more harmful than useful]]
- Thinking about [[Observational technological phenomena]] and the [[§Tech Tree Model of Heuretics]].
- Recording an occasional [Idea Machines Podcast](ideamachinespodcast.com)as a way to talk to fascinating people and share it with the world.

On the physical front, I feel like I’ve mostly got my incremental climbing/physical ability increasing [[Training Plan]] nailed down. The trick is recovery and not overeating.



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

Sarah Constantine conversation 8 Jul 2021

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

Week of 11 April 2022

Jerry Neumann conversation 1 July 2021

General-purpose telerobotics needs work that’s not suitable for a for-profit company

Apoorva Mehta conversation 10 Feb 2022

Software startups can get away with doing research

Week of 30 Aug 2021

14 Jul 2021

7 Jul 2021

9 May 2022

# When should an idea that smells like research be a startup?
# When should an idea that smells like research be a startup
*Notes about language.*
1. I’m going to be trying to use the word ‘uncertainty’ in the technical sense of* [[Knightian Uncertainty]] *that carries a lot more meaning than just “we don’t know.”
2. I’m referring to things as “ideas” (in the [[Activity Space]] sense) to avoid “projects” “programs” etc. because whether it looks more like a project or a program ([[The distinction between areas, programs, and projects]]) probably has something to do with whether it makes sense as a startup.  
---

We can say for certain some things about ideas that smell like research:
0. To some extent they are attempting to do something, usually discovery or invention-wise, that nobody has done before. In other words, they do not have a global [[Critical path]]. [^1]
1. They will not be able to generate cash flow directly from their main activities for some amount of time.  
2. As a result of #1, the activity will need some kind of money factory.[[Innovation orgs need a money factory]].
3. That money factory (or multiple money factories) will need to put money into the idea for some amount of time before it produces a ‘working’ result — a good business, a technology that other people can use, an acquisition etc.  
4. There is unpredictability about the timescale to get to a ‘working’ result. More uncertainty about critical paths means more unpredictability on the timeline, or even the timescale. (Keep the fact that [[Mismatched Buxton Indexes lead to shear forces]] in mind.) 
5. In large part because of the timescale unpredictability, there is unpredictability about the amount of money the idea needs to get to that ‘working’ result. Cost is often tightly coupled to the amount of time it takes. Hence, more critical path uncertainty -> more time unpredictability -> more cost unpredictability.
6. The time and money are necessary to address technology risk (a chance that the technology won’t hit a performance metric) and technology uncertainty (not knowing what the metric actually is). 
7. Researchy ideas have some amount of uncertainty about potential outcomes: what the form of the output will be, whether there is a market for that output, whether the output will be of a form that can capture value from that market, whether the program will organizationally be able to be a business that can do that value capture. 

If the idea is housed in a new for-profit organization, ￼#1+#2+#4 means that except in rare cases, the organization needs to be a high-growth startup because in order to provide competitive time-discounted ROI on the money the company needs to do the work over a potentially long timescale, it needs to target massive returns. Being a [[Grahamian startup]] then subjects the organization to [[§Startup Constraints]]. 

One could imagine situations where a for-profit research program could avoid committing to be a high-growth startup. A for-profit could get away with targeting lower growth/eventual value if funding comes from non-dilutive grants or ‘friendly’ investment where the investors don’t expect to make as much return on their money as they could elsewhere. However, both of these options come with their own downsides. These downsides include needing to keep investors happy because ‘friendly’ investment generally means ‘not market competitive’ so investors become much less replaceable. The danger is that the product becomes ‘things that make investors or grant-givers happy’ rather than things that maximize the potential of the idea and push the [[Knowledge frontier]].  (Or even make the most money in the long run.) Friendly-funded for-profits also experience a lot of pressure towards efficiency, and [[Efficiency biases systems towards false negatives]]. (Of course this efficiency bias is a cultural thing and not tied just to profit-seeking.)

Outliers dominate both the results and these conversations so I’m going to call out the elephants in the room:
- [[SpaceX]] (it’s even debatable whether this counts as an idea that smelled like research — at first they weren’t even trying to do something nobody had done before — there was a pretty clear but very hard critical path). 
- [[Genentech]]
- A number of therapeutic companies
- [[OpenAI]]
- [[DeepMind]] 
The fact that the list is so short compared to the list of researchy for-profits that have failed to deliver on their promises seems indicative. The ‘failure rate’ is even higher than the average for startups. This list also points to the fact that the question might be very discipline dependent. Only SpaceX is the non-therapeutic, non AI company on the list.  

At the end of the day, no set of criteria can definitively say “this project shouldn’t be carried forward by a new for-profit organization” except maybe if desired output could never be sold. (Though even then someone could point to profitable but unintended discoveries as a reason for researchy work to be a startup). The nature of [[Knightian Uncertainty]] means that it’s certainly possible to flip all heads in a row on a large-dimensional, non-stationary coin and pull off a successful research startup. However, I would argue that the more the unpredictability in each of the steps smells like uncertainty instead of risk ([[Uncertainty always involves risk but risk does not always involve uncertainty]]), the less it’s a good idea for the project to be carried forward by a new for-profit organization. 

A decision tree may be the correct way to look at the question of whether a program is too researchy to be a startup. These questions are ordered roughly in terms of importance, with “no” answers strongly indicating that a start:
- Can the desired output be sold?
- Is there a global [[Critical path]]?
- Is there an objective ‘measure’ of what ‘working’ means? How smooth is it?
- Is there a bounded amount of market uncertainty?
- Is the technology a modular product (ie. does it slot into existing systems)?
- Would investors consider a low ROI acceptable? 
- Would investors be willing to wait a long and unpredictable amount of time to get that return?
- Are investors ok with an illegible process without clear milestones? 

### Can the desired output be sold or unlock a product?
If you are setting out to create something that could never be sold, the program probably should not be a startup. There are examples of accidental but profitable discoveries that happened in the course of pursuing something out of motivations besides profit — discovering a heart drug while studying tree frog poison in the amazon for example — but you might as well just buy stocks chosen by a random number generator. 

### Is there a global critical path?
No global critical path means that you need to run a “fat” process without clear justification. (Justification requires explaining how it fits into a bigger picture which requires a global critical path). Without a global critical path, “progress” is nothing but a narrative and the fat process makes it look like you’re doing a lot of dicking around. As a result, investors will get fed up at some point and either force the research into a premature product or shut the whole thing down. 

### Is there a smooth objective measure of what ‘working’ means?
One way to look at uncertainty is that there’s no definition of what ‘works’ means: this could be in terms of the technology itself (technology uncertainty) or in terms of how it becomes a good business (market uncertainty). Note that this is different from technology or market *risk* where you know what winning looks like but there are a lot of reasons why you wouldn’t get there. [[Uncertainty always involves risk but risk does not always involve uncertainty]]. Technology risk sounds like “there’s a chance that we can’t get the efficiency of this process above 35% but the back of the envelope says there’s nothing that should prevent that and we have a development plan to get there.” (A metric and a vague sense of a probability distribution over that metric). 

Creating new technology will almost always involve *some* uncertainty. Technology’s modular/combinatorial nature[^2] means that there will be uncertainty at some level of the hierarchy. Sometimes that uncertainty will propagate up to the top-level technology if it is an irreplaceable subsystem. However, my extremely hand wavy intuition is that [[You cannot have massive uncertainty in subsystems without it propagating upwards]]. 

Even if you have a clear measure of what ‘working’ means, the way in which that metric changes matters. A smooth metric means that incremental changes can cumulatively move the needle (which means that you can show nice graphs trending towards success). However, some researchy ideas involve situations where the needle doesn’t move at all until it does a massive jump (ie. They depend on breakthroughs).  

### Is there a bounded amount of market uncertainty?
Situations where you’re guaranteed a massive market if you can hit a clearly defined target lend themselves to researchy startups. Theraputics are a prime example here[^3]: “if you can create a cancer drug that passes FDA approval, you are guaranteed a $B market.” In the past, mainframe computers had a bit of this flavor — if you could hit a clear performance metric, you had a guaranteed market. [[It is possible that healthtech and therapeutics in the early 2020s is to biotech what calculation and mainframes were to  computers in the 1960s]]. 

A big reason that market uncertainty can kill researchy startups is less about investment and more about the fact that [[There is a tradeoff between an organizational culture that is good at addressing market uncertainty and one that is good at addressing technological uncertainty]]. At some point a researchy startup needs to do a dramatic gear shift into growth and product-market fit mode. This transition often either prematurely kills the research potential or the company dies because it’s being run by people with a research mindset. 

### Is the technology a modular product (ie. does it slot into existing systems)?
[[Startups are good at point changes]]. If there is a component in a system that can be improved or swapped out in order to make the system cheaper/more profitable/more efficient, startups are an excellent mechanism for making that happen. The same goes for projects in industrial research labs — if you can, say, save the company a ton of money by swapping out the sheathing on telephone cables, the path to success is straightforward. However, to improve, many systems need many simultaneous changes (or to be swapped out for a new process entirely[^4]). 

[[Systems must take performance hits to get out of local optima]] and people are rarely willing to pay to *decrease* system performance. (This is a major contributor to [[The (idea) valley of death]]).

See also [[Systems research requires a lot of work that is expensive and uninteresting]]

### Would investors consider a low ROI acceptable? 
A lot of research involves dicking around, or “fat” processes. If you need to target a high ROI from day one, you need to be as efficient as possible. ROI is all about efficiency, and [[Efficiency biases systems towards false negatives]]. There will just be less [[Slack - concept]] in the system, cutting off lines of work that can’t be justified up front (especially if they require resources to evaluate). 

### Would investors be willing wait a long and unpredictable amount of time to get that return?
A key characteristic of research is that regardless of how long it takes, that amount of time is *unpredictable*.[^5] Combined with the fact that progress is often not smooth, it takes a constitution of steel for investors not to start getting antsy at some point. People are patient until they’re not. The pressure to show some progress towards a progress makes sense from a business sense but can be at odds with developing a powerful technology. Premature specialization is a common failure mode in researchy startups. ([[Ideally corporate R+D enables work on a general purpose technology before it’s specialized]]) .

### Are investors ok with an illegible process without clear milestones? 
In addition to unpredictable timelines, a lot of technical research involves constantly shifting targets. You can set milestones (and it is often useful to do so), but they need to be incredibly fluid. Unlike business progress, which can be boiled down to a few core metrics like customers or revenue regardless of how much pivoting is happening, research progress is much harder to boil down to a legible metric.  

Some questions that are proxies for the degree of technological uncertainty facing a project:
**How smooth is the technology’s fitness landscape? That is, how much could a single change shift outcomes?** 
[[The smoothness of a technology’s fitness landscape has a big effect on a program’s technological uncertainty]]
**Do you know what it means to improve?**  

**Do you know how you will know that you know how to incrementally improve the system?**

### Related
- A specific subset here is  [[General-purpose telerobotics needs work that’s not suitable for a for-profit company]]
- [[What does ‘working’ mean?]]
- [[Uncertain things basically have infinite risks]]
- [[Knightian Uncertainty]]
- [[There might be many situations with intractable uncertainty or randomness in the highest order bits]]
- [[§Startup Constraints]]
- [[Jerry Neumann conversation 1 July 2021]]
- [[Research is especially susceptible to Goodhart’s Law]]

### Braindump
- One trick is that the non-stationarity of the coin flip means that if the people on the program know that they only get a set number of flips before they produce a working thing
- [[Startups are the upper left corner of the divergent-convergent lean-fat 2x2]]
- Are we allowed to throw out “what about Tesla/SpaceX”
- There needs to be a lot of excess value creating from the research work 
- [[Startups need to conform to investor timescales]]
	- [[Venture capitalists need to fund things that increase valuation in a few years and return in 5-7 years]]
	- The average stock market return is 9.2% 
	- Average over 2010-2020 13.6%
		- $1->$6.77 over 15 years
		- In order to beat that by even 2x a startup would need to turn $100M of investment into $6.8B returned to investors, so valuation would probably need to be $10B
- Startups working on general purpose technology tend to get sidetracked
	- Magic Leap
	- 
- Manufacturing a physical thing that has never been manufactured before has not led to great results outside of electronics 
	-  [[Startups need to produce a scalable product]] 
		- “But what about Tesla and SpaceX” 
- Need an actual breakthrough, as opposed to just grinding on current paradigms 
- TRL of the overall system
- There’s also the point that [[Organizations get good at one thing]] — organizations that are optimized for doing exploratory research and finding critical paths 

[^1]: In other words, they are not in the lower left quadrant of  [[raoGUTSGrandUnified2018]]
[^2]: See [[arthurLogicInvention2005]]
[^3]: See [[Therapeutics have established sales channels]]
[^4]: See [[fillerFundamentalManufacturingProcess2020]]
[^5]: See [[narayanamurtiCyclesInventionDiscovery2016]] 

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