BAE. bit atom energy management 💡⚙️⚡️

[hyunjimoon]

@tomfid wish to review and fill out table with you!

[hyunjimoon]

Double helix from clockspeed

• how to connect our manufacturing template's four adjustment times with clockspeed? lower sl adj time and higher inv adj time, less oscillation

  • supply: supply line, inventory, backlog
  • demand: perception
    

• from the figure, when does modules become commodities?, core rigidities?

• how is advanced manufacturing (use of innovative technology to improve products and/or processes, and may also include the use of new business/management methodologies) different from canonical manufacturing
  

Dual pendulum : iterative diffusion of workflow

spinoff of double helix with focus on workflow, by angie

• is the following reasonable?

1. x-axis: variability ratio of supply to demand (bio < chem < mech) valid?meaning in bio, demand is relatively known (cure disease, engineering cells) but supply is hard hence low var(demand)/var(supply). which is why pharma industry is slow clock speed. we know next to nothing about life engineering, but identified demand are high and certain.

2. y-axis: softness degree; bits only (1) vs bits+atoms vs bits+atoms+energy

[hyunjimoon]

@chasfine how could we make your table below more richer with the three sources below?

	DIGITAL	PHYSICAL
Development	software, apps, and/or online services	Develop and set up products and processes, including sourcing raw materials, developing supply chains, and production lines.
Production	Upfront cost of development, servers, and other digital infrastructure	Continuous management of manufacturing & supply chains
Cost Structure/ Scalability	~Zero marginal costs for more copies; May require increased marketing reach	Unit production costs can be significant and increasing scale may require added capital investment
Distribution	online via app stores, websites, and/or cloud-based services	transportation to retailers or directly to consumers; requires logistics, inventory management, returns, etc.
Updates and Improvements:	Can be continuous, remote, and as visible or  invisible as desired without the user even noticing. Allows for rapid iteration based on user feedback and data analytics.	improvements or modifications require creating a new version of the product and potentially recalling/repairing the old one.
Data Collection and Analytics	Data collection and analytics can be engineering into the products and infrastructure to a significant degree	Unless the product has a digital/connected component, data collection may be challenging, especially when the product was not sold directly be the manufacturer.
End of Life	discontinue support or updates, shut down servers or online services. Data migration and user communication are key considerations.  Some labor redeployment.	discontinue production and factories, and dispose of unsold inventory. Environmental considerations, such as recycling or disposal of the product, are also important.  Significant layoffs possible.
Entry Barriers	Competitors may be able to enter more easily.  Including pirates.	Manufacturing and supply chain capabilities may create higher barriers to entry

1. a quote from inmates running asylum

I believe that we really are in a new economy. What's more, I think that the dot-coms never even participated in it. Instead, the dot-coms were the last gasp of the old economy: the economy of manufacturing.
In the industrial age, before software, products were manufactured from solid material-from atoms. The money it took to mine, smelt, purchase, transport, heat, form, weld, paint, and transport dominated all other expenditures.
Accountants call these "variable costs" because that expense varies directly with each product built. "Fixed costs," as you might expect, don't vary directly and include things such as corporate administration and the initial cost of the factory.
The classic rules of business management are rooted in the manufacturing traditions of the industrial age. Unfortunately, they have yet to address the new realities of the information age, in which products are no longer made from atoms but are mostly software, made only from the arrangements of bits. And bits dont follow the same economic rules that atoms do.
Some fundamental truths hold for both the old and the new economies. The goal of all business is to make a sustainable profit, and there is only one legal way to do so: Sell some goods or services for more money than it costs you to make of acquire them. It follows that there are two ways to increase your profitability.
Either reduce your costs or increase your revenues. In the old economy, reducing your costs worked best. In the new economy, increasing your revenue works much, much better. today’s most vital and expensive products are made largely or completely of software. They consume no raw materials. They have no manufacturing cost. They have no transportation cost.

2. analysis from a textbook Erin Scott recommended on econ strategy:

1. Economics of density
2. Purchasing
3. Advertising
4. Research and development
5. Physical properties of production
6. Inventories
   The first four rely entirely or in part on spreading of fixed costs. Physical properties of production and inventory-based economies do not.

Physical Properties of Production
Economies of scale may arise because of the physical properties of processing units. An important example of this is the cube-square rule, well-known to engineers. 6 It states that as the volume of the vessel (e.g., a tank or a pipe) increases by a given proportion (e.g., it doubles), the surface area increases by less than this proportion (e.g., it less than doubles).
The cube-square rule is not related to spreading of fixed costs. So what does the cube-square rule have to do with economies of scale? In many production processes, production capacity is proportional to the volume of the production vessel, whereas the total cost of producing at capacity is proportional to the surface area of the vessel. This implies that as capacity increases, the average cost of producing at capacity decreases because the ratio of surface area to volume decreases. More generally, the physical properties of production often allow firms to expand capacity without comparable increases in costs.
Oil pipelines are an excellent example of this phenomenon. The cost of transporting oil is an increasing function of the friction between the oil and the pipe. Because the friction increases as the pipe’s surface area increases, transportation costs are proportional to the pipe’s surface area. By contrast, the amount of oil that can be pumped through the pipe depends on its volume. Thus, the average cost of a pipeline declines as desired throughput increases. Other processes that exhibit scale economies owing to the cube-square rule or related properties include warehousing (the cost of making the warehouse is largely determined by its surface area) and brewing beer (the volume of the brewing tanks determines output).

3. referee report paper from 15.357

Baruffaldi, Stefano and Fabian Gaessler. 2021. “The Returns to Physical Capital in Knowledge Production: Evidence from Lab Disasters.” Max Planck Institute for Innovation & Competition Research Paper No. 21-19. Available at https://ssrn.com/abstract=3912401.

[hyunjimoon]

I found attached paper from 15.357 reading list interesting as it explains hardware makes entrepreneur financially constrained (capital up-front), increasing the grant effect for hardware firms  (Table 7, column 3). However, there is no comparable effect on survival.
Also, "Emerging versus mature sector" is interesting as in this diagram from #159, two axis of industry (env) were clockspeed and trend of industry.

Howell, Financing Innovation.pdf and table 7 from the paper

[hyunjimoon]

evaluation measures of bit, atom, energy (capital stock) of startup: pitchbook_var.pdf

[hyunjimoon]

how humans outsourced body, brain, mind to communication, energy, logistics internet (Moon, 2023)

[hyunjimoon]

Jungpil

further reach out
vc: vishal

researcher
research on early startup case studies: https://www.comp.nus.edu.sg/disa/bio/sylim/
nus enterprise committee: https://www.comp.nus.edu.sg/disa/bio/hengcs/

[hyunjimoon]

how would the following change for hardware?

1. diagram (operation)
   q1. does longer planning delay require less energy? depends on whether it's infinite order delay or smoothing (3rd degree) delay. if latter, the longer, the more energy as it need to keep track of past data.
   q2. if q1 is yes, can we the problem as allocating resource to three tasks (release, planning, reengineering) across 100 weeks? e.g. release cycle is 4, planning delay is 2, reengineering delay is 1 (week) then the firm allocates its energy to each task as 1/7:2/7:4/7.
   

2. interpretation
   2.1 release cycle lengths
   2.2 planning delay
   2.3 reengineering capacity

[hyunjimoon]

To answer resource allocation to bit and atom across a startup's growth phase, I decomposed them into three questions and made a table for each:

1. how does startup allocate resource between bits and atoms conditional on the environment (industry/market)'s atomness? BAE1. argmax_x (u|x, theta's atomness) industry effect #190
2. how does startup sample and learn conditional on the environment? BAE2. x| theta #191
3. how does startup choose to change its environment? BAE3. update or updated psi (environment): #192

table1: bit and atom

Building on Table1, I wish to understand resource allocation between startup's subcomponents given the environment. My hypothesis is industry's atomness affect startup operation greatly. Extending #147 (comment) which compared how atom (physical) and bit ( digital) character affects startup operations in across lifecycle (development to end of life + entry barrier) I used analogical structure of DIGITAL:PHYSICAL = BIOTECH:SEMICONDUCTOR to flesh out the comparison. Moreover, framing these with biological analogies (cell, ecosystem, evolution, phylogeny, life) helped me understand startup biology and evolution. Dynamic feature in evolution helps us understand/formulate successful startup pivot by benchmarking across industry described in #100.

table2: learn and grow

Building on Table2, I wish to understand how agent learn from environment and grow.

table3: pivot

This investigates how a startup choose to change its environment. My hypothesis is, we can list conditions of successful pivot by combining Table1 and Table2. Moreover, we can answer, How is planned pivot (strategic) and improvised pivot differ? Where does Moderna's pivot which shifted from mRNA technology for therapy to vaccine development upon Covid lie on this spectrum? Could we understand successful and failed pivots in unified framework?

Building on expative innovation discussed in #184 (comment), we may need counterfactual (pivot vs non-pivot) which may be approached by modularized timeseries with e.g. gaussian process (as in Birthday problem here which can be represented as model network in sec.9.3 of Multi-Model Probabilistic Programming). Using the analogy of sampling algorithm and startup learning during its growth, I built table2 which tries to use sampling algorithm's learning phase to give quantifiable (at least ordinal enough to turn causal loop into tree that identifies bottleneck) to current nail, scale, sail framework by Charlie.

[hyunjimoon]

[hyunjimoon]

Category	Aspect	Physics	Psychology
Nature of Problem
	State Space	Limited, well-defined dimensions	High-dimensional, unclear boundaries
	Reference System	Fixed spatial/temporal coordinates	No universal framework
	Transition Rules	Deterministic/clear probabilistic	Complex, context-dependent
Individual Factors
	Prior Impact	Minimal on observations	Shapes interpretation significantly
	Observer Effect	Independent of observer	Observer-belief dependent
	Measurement	Direct access to phenomena	Partial observation of internal states
Institutional/Social
	Training Data	Consistent across contexts	Varies by culture, time, situation
	Validation Method	Falsifiable point predictions	Directional effect detection
	Error Handling	Narrows with precision	Expands with complexity
Statistical Properties
	Null Hypothesis	Point prediction (x = x0)	Directional (x ≤ 0)
	Type 1 Error	False point prediction rejection	False directional effect detection
	Type 2 Error	Missing true deviation	Missing true effect
	Precision Impact	Reduces both error types	May increase Type 1 error
	Success Criterion	Surviving stringent tests - everything in red is interpreted as falsifying the theory - if xbar != xbar_0, probability we will find a supportive finding approaches 0 with increasing sample size	Any non-zero difference - everything in red is interpreted as validation of the theory - if x has no effect on y, probability we will find a supportive finding approaches 0.5 with increasing sample size

image for success criterion row:

[hyunjimoon]

.

[hyunjimoon]

Chapter Number	Chapter Title	Key Message Summary	Key Figures
1	Why Hardware Takes So Much Longer Than Software	Explains the fundamental differences between hardware and software development cycles, highlighting the longer iteration times, physical constraints, and higher costs associated with hardware development. Shows why hardware requires more upfront planning and careful consideration.	N/A
2	On Primary Market Research	Details the crucial importance of thorough market research before committing to hardware development. Covers methods for understanding customer needs, market size, and competitive landscape to validate product concepts before significant investment.	N/A
3	The Development Phases	Outlines the sequential stages of hardware product development, from initial concept through prototyping and testing. Emphasizes the importance of proper phase-gate processes and iterative development to minimize costly mistakes.	N/A
4	The Manufacturing Phases	Explores the transition from development to manufacturing, including Design for Manufacturing (DFM), supplier selection, tooling, and quality control processes. Details how to scale from prototypes to mass production while maintaining quality.	Figure 13: Timeline diagram showing the complete product development process divided into two main phases: - Development Focus (blue): Including Discovery Research, Science Projects, EMC Tests, Prototypes, Engineering Prototype, and Engineering Verification - Manufacturing Focus (green): Shows progression through Design Verification (DV), Process Verification (PV), and Mass Production (MP) Notable features: - Continuous Product Research bar spanning entire timeline - Clear transition point between development and manufacturing focus - Sequential progression through verification stages
5	Organizing The Troops	Addresses the organizational structure and team composition needed for successful hardware development. Covers roles, responsibilities, and how to build effective cross-functional teams spanning engineering, manufacturing, and business functions.	N/A
6	A Word On Customer Development	Focuses on the continuous process of customer engagement throughout product development, ensuring the final product meets market needs and has product-market fit. Discusses methods for gathering and incorporating customer feedback despite hardware's longer development cycles.	N/A

[hyunjimoon]

a1. share belief and desire (plan for revise book?)
a2. share strat, ops
a3. choice of experiments and institutions role

[hyunjimoon]

elaine chen recommended to look into

• 
• talk with ten random entrepreneurs
• very insightful chat! 🙏