# BAE. bit atom energy management 💡⚙️⚡️ from hyunjimoon on 2023-06-02T22:03:30Z @tomfid wish to review and fill out table with you!  --- ## Reply from hyunjimoon on 2023-06-05T19:29:12Z ## 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 <img width="444" alt="image" src="https://github.com/Data4DM/BayesSD/assets/30194633/6a23cb1b-b5ef-4d11-b45a-0ab595e7ebf4"> --- ## Reply from hyunjimoon on 2023-09-16T14:40:58Z @chasfine how could we make your table below more richer with the three sources below? | | SW | HW | | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 💻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](https://www.amazon.com/Inmates-Are-Running-Asylum-Products/dp/0672326140) > 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]( https://www.amazon.com/Economics-Strategy-7th-David-Dranove-ebook/dp/B01AKSZ952 ): 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. --- ## Reply from hyunjimoon on 2023-11-27T00:20:35Z 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](https://private-user-images.githubusercontent.com/30194633/262426992-410000a2-1bfd-438c-8027-c505f88fa14d.png?jwt=*** diagram from #159, two axis of industry (env) were clockspeed and trend of industry. [Howell, Financing Innovation.pdf](https://github.com/Data4DM/BayesSD/files/13468521/Howell.Financing.Innovation.pdf) and table 7 from the paper <img width="300" alt="image" src="https://github.com/Data4DM/BayesSD/assets/30194633/79d53e85-87c3-4b32-b448-3190cadbfd00"> --- ## Reply from hyunjimoon on 2023-12-11T04:56:36Z evaluation measures of bit, atom, energy (capital stock) of startup: [pitchbook_var.pdf](https://github.com/Data4DM/BayesSD/files/13631113/pitchbook_var.pdf) --- ## Reply from hyunjimoon on 2023-12-16T05:39:08Z how humans outsourced body, brain, mind to communication, energy, logistics internet (Moon, 2023) <img width="1367" alt="image" src="https://github.com/Data4DM/BayesSD/assets/30194633/88136b59-5027-4b4d-b29b-b175070a03de"> --- ## Reply from hyunjimoon on 2023-12-20T08:09:47Z Jungpil <img width="620" alt="image" src="https://github.com/Data4DM/BayesSD/assets/30194633/d11d869f-cedf-450c-ae6b-5e1cd1050237"> 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/ --- ## Reply from hyunjimoon on 2023-12-26T10:41:22Z 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.  3. interpretation 2.1 release cycle lengths 2.2 planning delay 2.3 reengineering capacity --- ## Reply from hyunjimoon on 2024-02-22T12:25:02Z 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? #190 2. how does startup sample and learn conditional on the environment? #191 3. how does startup choose to change its environment? #192 <img width="1056" alt="image" src="https://github.com/Data4DM/BayesSD/assets/30194633/4795e4bb-ba08-4cc5-9d1a-3819cf74b875"> ## table1: bit and atom Building on [Table1](https://github.com/Data4DM/BayesSD/discussions/190#discussion-6254456), I wish to understand resource allocation between startup's subcomponents **given the environment**. My hypothesis is industry's atomness affect startup operation greatly. Extending https://github.com/Data4DM/BayesSD/discussions/147#discussioncomment-7022032 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](https://github.com/Data4DM/BayesSD/discussions/191#discussioncomment-8558622), 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 https://github.com/Data4DM/BayesSD/discussions/184#discussioncomment-8494869, 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](https://htmlpreview.github.io/?https://github.com/avehtari/casestudies/blob/master/Birthdays/birthdays.html) which can be represented as model network in sec.9.3 of [Multi-Model Probabilistic Programming](https://arxiv.org/pdf/2208.06329.pdf)). 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. --- ### Reply from hyunjimoon on 2024-02-22T13:22:06Z ## table1: bit and atom | **Aspect** | **DIGITAL** | **PHYSICAL** | **BIOTECH** | **SEMICONDUCTOR** | | --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | | **Development** | Software, apps, and/or online services | Develop and set up products and processes, including sourcing raw materials, developing supply chains, and production lines. | Focused on research & development in labs, clinical trials for drug discovery, genetic engineering. | Design and development of semiconductor devices, prototyping, and engineering for manufacturing. | | **Production** | Upfront cost of development, servers, and other digital infrastructure | Continuous management of manufacturing & supply chains | Small scale for clinical trials, can scale up with contract manufacturing organizations. | High initial setup costs for fabs, ongoing costs for materials, and precision manufacturing processes. | | **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 | Lower marginal costs for drug production once approved; scalability dependent on regulatory approval. | Significant investment in fabrication plants; economies of scale important for cost reduction. | | **Distribution** | Online via app stores, websites, and/or cloud-based services | Transportation to retailers or directly to consumers; requires logistics, inventory management, returns, etc. | Distribution through healthcare providers, pharmacies; regulatory compliance for market access. | Distribution through technology product manufacturers, direct sales to electronics companies. | | **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. | Updates through additional research, new product versions; continuous improvement in treatment efficacy. | Product iterations require new manufacturing cycles; updates often coincide with new product releases. | | **Data Collection and Analytics** | Data collection and analytics can be engineered 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. | Increasing use of digital health technologies for data collection in clinical trials and patient monitoring. | Embedded sensors and IoT integration enable data collection for some semiconductor products. | | **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. | Discontinuation of older drugs, biologics; focus shifts to newer treatments. Environmental impact of disposal considered. | End of product lifecycle management, recycling of electronic components; significant environmental considerations. | | **Entry Barriers** | Competitors may be able to enter more easily. Including pirates. | Manufacturing and supply chain capabilities may create higher barriers to entry | High due to regulatory hurdles, IP protections, and the need for specialized knowledge. | Very high due to the cost of fabrication plants, specialized equipment, and technical expertise. | | **Cell Theory** | Platforms enabling individual developers to focus on niche digital solutions. | Startups focusing on developing specific hardware technologies. | Individual biotech firms focusing on niche healthcare solutions. | Startups developing specific semiconductor technologies. | | **Ecosystem** | Digital marketplaces and developer communities supporting collaboration. | Industrial ecosystems involving suppliers, manufacturers, and distributors. | Collaboration with healthcare systems, regulatory bodies, and research. | Interactions with suppliers, manufacturers, and tech companies. | | **Evolution** | Rapid iteration and innovation in software based on user feedback. | Gradual technological advancements through R&D and market feedback. | Innovation in response to healthcare challenges and regulations. | Technological advancements and market adaptation. | | **Phylogeny** | Tracing the development and influence of major software and internet technologies. | Tracing the history and evolution of manufacturing processes and materials. | Tracing the development of critical medical technologies. | Evolution of chip manufacturing techniques. | | **Properties of Life** | Digital products growing through user adoption, evolving with market needs. | Physical products growing through market penetration, evolving with manufacturing innovations. | Growth through research progress, reproduction via spin-offs, response to healthcare trends. | Growth through technological innovation, reproduction through patents, adaptation to tech trends. | --- ### Reply from hyunjimoon on 2024-02-22T13:28:16Z ## table2: learn and grow | Concept | Sampling Algorithm | Biotech Startup | Semiconductor Startup | |------------------------|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------| | **Nail** | Initial convergence towards the typical set with parameters learning from local information. | Identifying therapeutic targets and beginning drug discovery based on direct feedback. | Designing a novel microchip and iterating designs based on initial test feedback. | | **Scale** | Estimation of global parameters, like covariances, through expanding, memoryless windows. | Conducting extensive clinical trials and navigating regulatory processes. | Ramping up manufacturing and establishing supply chain logistics. | | **Sail** | Adaptation of fast parameters to the final update of slow parameters. | Expanding market reach and optimizing production based on final drug profile. | Refining chip designs for efficiency and cost-effectiveness. | | **Chain** | Markov Chain | Development pathway from discovery through clinical trials. | Process from initial design to final manufacturing. | | **Typical Set** | Convergence target in parameter space. | Effective therapeutic profile meeting safety and efficacy standards. | Optimal chip design meeting performance and manufacturing criteria. | | **Parameters** | Model variables adjusted based on data. | Drug formulation components, dosage levels. | Microchip architectural features, material specifications. | | **Local Information** | Data influencing immediate parameter adjustments. | Early-stage trial results, initial market feedback. | Prototype testing outcomes, initial customer feedback. | | **Covariance** | Measure of parameter interaction and dependency. | Relationships between clinical trial outcomes, market responses. | Interdependencies between chip components, performance metrics. | | **Expanding Window** | Increasing data scope for parameter estimation. | Gradual inclusion of broader trial data, market analysis over time. | Expansion of manufacturing capabilities, market presence based on accumulating data. | | **Primary Focus** | Model optimization and efficiency in exploring parameter space. | Research and development of drugs, therapies, or medical technologies. | Design and manufacturing of electronic components. | | **Development Timeline** | Time required to achieve stable model convergence and parameter optimization. | Over a decade due to research, clinical trials, and regulatory approval. | Few years from design to manufacturing. | | **Regulatory Environment** | Adaptation to evolving statistical standards and data privacy regulations. | Highly regulated with need for FDA approval. | Less regulated, focuses on quality and manufacturing standards. | | **Capital Requirements**| Investment in computational resources and data acquisition. | High due to long development times and clinical trials. | Significant investment in R&D and fabrication plants. | | **Risk Profile** | Risk of model inadequacy or failure in capturing complex data relationships. | High risk of product not reaching market due to clinical/regulatory hurdles. | High risk due to technology trends and market demand. | | **Market Dynamics** | Influence of new algorithms, computational techniques, and data availability on model relevance and application. | Challenging entry but substantial rewards for successful products. Patent protection creates monopolies. | Fast-paced and competitive, dependent on innovation and market penetration. | | **Revenue Generation** | Potential for model application across different data sets and scenarios for insights. | Long investment period with no revenue, spikes upon product launch. | Steady revenue once products hit the market, based on innovation and demand. | | **Key Success Factors** | Efficient algorithm design, computational resource management, and adaptability to new data. | Innovation in R&D, successful regulatory navigation, partnerships for development and distribution. | Technological innovation, manufacturing efficiency, supply chain management, market penetration. | --- ### Reply from hyunjimoon on 2024-02-22T22:09:53Z Kenoteq is developing eco-friendly bricks made from construction waste, offering a sustainable alternative to traditional bricks with lower carbon emissions​​. Basilisk Concrete has introduced a line of self-healing concrete products that utilize microorganisms to repair cracks in concrete, enhancing durability and sustainability​​. Eave focuses on protecting construction workers from hearing loss through smart ear defenders and noise visualization platforms​​. ViAct leverages AI for construction site monitoring, enhancing safety and compliance by identifying potential hazards​​. Fieldwire offers a user-friendly construction management app that facilitates task assignment, project tracking, and document updating​​. Procore provides a cloud-based platform for managing the entire construction process, aiming to increase collaboration and efficiency​​. Sparkel, a startup from Norway, simplifies construction drawing interpretation, enhancing decision-making and project management​​. ecopals introduces EcoFlakes, a sustainable road building material made from recycled plastics, aiming to improve road durability while reducing environmental impact​​. Arqlite focuses on recycling non-recyclable plastics into construction and landscaping materials, offering a sustainable alternative to traditional gravel​​. Plant Prefab prioritizes sustainable prefabricated homes, partnering with architects and homebuyers to deliver environmentally friendly housing solutions​​. --- ## Reply from hyunjimoon on 2024-11-20T12:15:28Z  --- ## Reply from hyunjimoon on 2024-11-20T12:16:48Z | 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<br><br><br><br>- everything in red is interpreted as falsifying the theory<br>- if xbar != xbar_0, probability we will find a supportive finding approaches 0 with increasing sample size | Any non-zero difference<br><br><br>- everything in red is interpreted as validation of the theory<br>- 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: <img width="592" alt="image" src="https://github.com/user-attachments/assets/f1c3f7e2-b330-460b-87aa-7af9a8a079ec"> --- [Discussion link](https://github.com/Data4DM/BayesSD/discussions/147) # [[2025-11-19]] extending [[Front/On/love(cs)/thesis_v1/📝product/00_TOC]], [[transport(toc1, toc2)]] ### Comparison of Software vs. Hardware Startups | Factor | Software | Hardware | | :------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **1. Experimentation Cost (c) & Value Inflection** | - **Low Cost (c↓):** Cloud computing allows for cheap, early experiments and "long-shot bets." <br><br>- **High Option Value:** Even with high failure rates, the low cost makes initial experiments valuable.<br><br>- **Clear Value Inflection:** Successful experiments can lead to massive step-ups in valuation. | - **High Cost (c↑):** Early experiments remain expensive, despite advances like 3D printing. <br><br>- **Noisy Signals:** Lab results are less predictive of commercial scalability. <br><br>- **Reduced Attractiveness:** Higher costs and lower predictive power make iterative testing less attractive to investors. | | **2. Pivoting Cost (k) & Irreversibility** | - **Near-Zero Cost (k≈0):** Changes are modular. Code can be rewritten and features adjusted without scrapping physical assets. <br>- **Standard Procedure:** The "pivot" is a common and accepted strategy. | - **High Cost (k≈1k \approx 1k≈1):** Decisions are "sticky" and create high irreversibility. <br><br>- **Sunk Costs:** Committing to a manufacturing process or architecture means changing it requires scrapping tooling, inventory, and re-doing certifications. <br><br>- **Investor Fear:** The high cost of failure makes it harder to finance strategies that might require a pivot. | | **3. The Persuasion Gap (Value of Information)** | - **Tolerance for Vagueness:** Investors accept some ambiguity because the next milestone (e.g., user traction) is a clear, objective signal of commercial viability. | - **Demand for Precision:** The definition of a "successful result" is often ambiguous due to a lack of established benchmarks. <br>- **Penalty for Vagueness:** Investors demand specific technical milestones to reduce ambiguity, penalizing founders who might prefer strategic flexibility. | Based on Nanda (2024) and Gans et al. (2019), the difference between Software (SW) and Hardware (HW) isn't just about "code vs. atoms," but about the **economic structure of learning and commitment**. 제시해주신 **DPCDUDEE (Development, Production, Cost, Distribution, Updates, Data, End-of-Life, Entry)** 분류표는 하드웨어(HW)와 소프트웨어(SW)의 구조적 차이를 적나라하게 보여줍니다. 결론부터 말씀드리면, 이 표는 **실험 비용 ($c$)**과 **피벗 비용 ($k$)** **모두와 직접적인 관련**이 있습니다. 하지만 표의 항목별로 그 무게중심이 다릅니다. ### 1. 분석: 표의 항목은 $c$와 $k$ 중 어디에 해당하는가? 이 표는 **"SW는 가볍고($c \downarrow, k \downarrow$), HW는 무겁다($c \uparrow, k \uparrow$)"**는 것을 증명하는 증거 자료입니다. - **실험 비용 ($c$, Experiment Cost)과 관련된 항목:** - **💻 Development:** SW는 코드만 짜면 되지만(Low $c$), HW는 원자재 소싱과 라인 구축이 필요합니다(High $c$). 초기 시제품을 만드는 비용 자체가 다릅니다. 1111 - **🧐 Data Collection:** SW는 데이터 수집이 내재화되어 있어 반응을 즉시 보지만(Low $c$), HW는 연결되지 않은 제품의 데이터를 얻기 어렵습니다(High $c$). 즉, "신호를 얻는 비용"이 다릅니다. - **피벗 비용 ($k$, Pivoting Cost / Irreversibility)과 관련된 항목:** (이 부분이 결정적입니다) - **🔧 Updates:** SW는 원격으로 몰래 고치면 되지만(Low $k$), HW는 리콜(Recall)이나 폐기를 해야 합니다(High $k$). **"고치는 비용"**의 차이입니다. - **⚙️ Production & 📊 Distribution:** SW는 서버비만 내면 되지만, HW는 재고(Inventory)와 물류(Logistics)가 묶입니다. 방향을 틀면 이 재고가 다 **매몰 비용(Sunk Cost)**이 됩니다(High $k$). - **🪦 End of Life:** SW는 서버 끄면 끝이지만, HW는 공장 폐쇄와 폐기물 처리를 해야 합니다. 들어가는 문보다 나가는 문이 좁습니다(High $k$). --- ### 2. 교통(Transportation) 분야 예시를 통한 쉬운 설명 이 차이를 **"내비게이션 앱(SW)"**과 **"전기차 제조(HW)"**의 창업자로 비교해 설명하겠습니다. #### **(1) 실험 비용 ($c$): "첫 번째 시도의 가벼움"** - **📱 내비게이션 앱 (SW):** - 창업자는 노트북 하나로 코드를 짜서 앱스토어에 올립니다. (Development) - 사용자가 버튼을 누르는지 안 누르는지 로그 데이터가 공짜로 들어옵니다. (Data Collection) - $\to$ **실험 비용($c$)이 매우 낮습니다.** 싸게 찔러볼 수 있습니다. - **🚗 전기차 제조 (HW):** - 창업자는 금형을 파고, 배터리를 사오고, 공장 라인을 깔아야 시제품 한 대가 나옵니다. (Development) - 차가 도로에서 어떻게 달리는지 알려면 수천 km 주행 테스트를 직접 해야 합니다. (Data Collection) - $\to$ **실험 비용($c$)이 매우 높습니다.** 한 번 찔러보는데 억 소리가 납니다. #### **(2) 피벗 비용 ($k$): "실수를 바로잡는 대가"** - **📱 내비게이션 앱 (SW):** - "길안내 기능"이 별로라 "배달 기능"으로 바꾸려 합니다. - 그냥 코드를 업데이트해서 배포(Push)하면 끝입니다. 기존 사용자는 앱이 바뀐 줄도 모르게 넘어갑니다. (Updates) - 기존 코드는 지우면 그만입니다. (End of Life) - $\to$ **피벗 비용($k$)이 0에 가깝습니다.** 언제든 태세 전환이 가능합니다. - **🚗 전기차 제조 (HW):** - "세단"을 만들었는데 안 팔려서 "트럭"으로 바꾸려 합니다. - 이미 찍어둔 세단 **재고 1,000대**는 고철이 됩니다. (Distribution/Inventory) - 세단용으로 만든 **금형과 로봇팔**은 트럭에 못 쓰니 다 버려야 합니다. (Production) - 이미 팔린 차에 결함이 발견되면 **전량 리콜**해야 합니다. (Updates) - $\to$ **피벗 비용($k$)이 1에 가깝습니다.** 한 번 방향을 정하면(Commit), 돌이키는 데 회사의 운명을 걸어야 합니다. ### 🎖️ 요약 장군, 이 표는 **하드웨어 창업자가 왜 그토록 신중해야 하는지(T2C1의 필요성)**, 그리고 **소프트웨어 창업자가 왜 그렇게 기민할 수 있는지(Just Do It)**를 **'비용 구조'**로 완벽하게 설명해줍니다. - **SW:** $c$도 낮고 $k$도 낮음 $\to$ "일단 하고 고쳐라." - **HW:** $c$도 높고 $k$도 높음 $\to$ "신중하게 실험(T2C1)하고, 한 번 정하면 목숨 걸고 밀어라." (그런데 투자자는 이 $c$를 안 주려고 하니 비극인 것입니다.)