[New Publication] Inference Economy Shock 2026-2030: 'Physical AI' Turning Physical Limits into Profit and the Full Picture of Bottleneck Investment - Published by CMC Research
CMC Research has released a new report, 'Inference Economy Shock 2026-2030.' As AI shifts from training to the inference phase, this report presents investment strategies that turn physical limits—such as power, heat, and materials—into profit, alongside a comprehensive view of 'Physical AI.'
📋 Article Processing Timeline
- 📰 Published: April 28, 2026 at 20:10
- 🔍 Collected: April 28, 2026 at 11:31
- 🤖 AI Analyzed: April 28, 2026 at 13:53 (2h 21m after Collected)
➢ Investment criteria are changing drastically! The full picture of 'unmarketed technologies' where capital should go in 2026!
➢ Semiconductors, thermal management, spatial intelligence! Turning physical bottlenecks into the 'strongest entry barriers'!
➢ The era of learning is over! The roadmap for the 'Inference Economy,' where power is converted into profit, is here!
➢ Power, heat, materials! The 'physical limits' that bind digital intelligence are the very sources of enormous wealth!
➢ API-ize field assets! Physical AI will rewrite the power map of next-generation industries!
➢ Who comes after GAFA? Strategies for winners to premiumize 'computing resources' under grid constraints!
➢ Turning geopolitical risks into gains! The true nature of cold economics that turns regulation and standardization to its advantage!
🔗 Click here for details and purchase ▶
📘 Book Overview
Title: Inference Economy Shock 2026-2030: Turning Physical Limits into Profit with 'Physical AI' and the Full Picture of Bottleneck Investment
Publication Date: April 21, 2026
Format: A4 size, paperbound, 33 pages
Price: Main body (booklet) 66,000 yen (tax included)
Set Price (Book + PDF version CD): Main body + CD (PDF version) 110,000 yen (tax included)
ISBN: 978-4-910581-85-9
Edited and Published by: CMC Research Co., Ltd.
📝 Features of This Book
An era where constraints on semiconductors, thermal management, and power grids generate 'excess profits.'
In the inference economy, the source of value shifts to 'physical constraints'—presenting unmarketed technologies and capital allocation strategies that convert bottlenecks in power, heat, and materials into profit from a 2030 perspective.
An investment report that quantifies the entry barriers formed by Physical AI and the asset value of unmarketed technologies, clearly identifying the winning layers.
◎ Upon Publication
The transition to the 'Inference Economy' is no longer just a prediction but an inevitable reality from 2026 to 2030. As the market's center of gravity moves from the training phase to the inference phase, we face an ironic paradox. While digital intelligence seeks to multiply infinitely, the 'physical limits' of power, heat, and materials that support it have become absolute defining factors for growth.
This report is an attempt to redefine these physical limits not as costs or risks, but as 'bottleneck assets' that generate enormous premiums. In Chapter 3, 'Power x Compute,' we analyze how limited computing resources will become high-value-added under grid constraints. The 'physical bottlenecks' detailed in Chapter 6 are the biggest entry barriers in the inference economy, and the technologies that master this area will guarantee the true asset value of the 'unmarketed technologies' covered in Chapter 2.
This book presents a concrete roadmap for how physical field assets are integrated as digital APIs (Chapter 4) and how geopolitical regulations are converted into economic gains (Chapter 7). Heading toward 2030, where should capital be invested? The criteria for judgment are no longer the superiority of software, but whether the cold physical layer bottlenecks can be converted into 'profit.'
📖 Table of Contents Summary
Chapter 1: Executive Digest
1. Investment phases and profit sensitivity
2. Matching regional strategies with demand causality
3. Bargaining power and pricing authority within the value chain
4. Technical barriers: Differentiation in heat countermeasures and hedge structures
5. Execution framework: Capital allocation optimization and divestment criteria
6. Conclusion: Why Now? (Structural proof of waiting costs)
Chapter 2: Asset Value of Unmarketed Technologies - Quantifying Potential Gains
1. Scoring 'signs' and monitoring systems
2. Redefining cost structure and 'residual value' through R&D autonomy
3. Remaining 'physical filling' and regional roadmaps
4. Deciphering investment 'wait time': PLT (Physical Lead Time) model
Chapter 3: Power x Compute - Premiumization under Grid Constraints
1. Rising power unit prices and the economics of 'computation sparsity'
2. End-to-End evaluation of 1500VDC systems and next-gen power devices
3. Revenue model for integrated power and computation (Power-to-Inference)
4. Investment phases by time axis and risk symmetry matrix
Chapter 4: Physical AI and Spatial Intelligence - API-izing Field Assets
1. Digital twinning of physical assets: Interface layer turning equipment into 'computing resources'
2. Commercialization scenarios for mobile intelligence: From remote control to full autonomy - The scramble for the labor replacement market
Chapter 5: Autonomy and Minimizing External Dependency - Redefining the Supply Chain
➢ Semiconductors, thermal management, spatial intelligence! Turning physical bottlenecks into the 'strongest entry barriers'!
➢ The era of learning is over! The roadmap for the 'Inference Economy,' where power is converted into profit, is here!
➢ Power, heat, materials! The 'physical limits' that bind digital intelligence are the very sources of enormous wealth!
➢ API-ize field assets! Physical AI will rewrite the power map of next-generation industries!
➢ Who comes after GAFA? Strategies for winners to premiumize 'computing resources' under grid constraints!
➢ Turning geopolitical risks into gains! The true nature of cold economics that turns regulation and standardization to its advantage!
🔗 Click here for details and purchase ▶
📘 Book Overview
Title: Inference Economy Shock 2026-2030: Turning Physical Limits into Profit with 'Physical AI' and the Full Picture of Bottleneck Investment
Publication Date: April 21, 2026
Format: A4 size, paperbound, 33 pages
Price: Main body (booklet) 66,000 yen (tax included)
Set Price (Book + PDF version CD): Main body + CD (PDF version) 110,000 yen (tax included)
ISBN: 978-4-910581-85-9
Edited and Published by: CMC Research Co., Ltd.
📝 Features of This Book
An era where constraints on semiconductors, thermal management, and power grids generate 'excess profits.'
In the inference economy, the source of value shifts to 'physical constraints'—presenting unmarketed technologies and capital allocation strategies that convert bottlenecks in power, heat, and materials into profit from a 2030 perspective.
An investment report that quantifies the entry barriers formed by Physical AI and the asset value of unmarketed technologies, clearly identifying the winning layers.
◎ Upon Publication
The transition to the 'Inference Economy' is no longer just a prediction but an inevitable reality from 2026 to 2030. As the market's center of gravity moves from the training phase to the inference phase, we face an ironic paradox. While digital intelligence seeks to multiply infinitely, the 'physical limits' of power, heat, and materials that support it have become absolute defining factors for growth.
This report is an attempt to redefine these physical limits not as costs or risks, but as 'bottleneck assets' that generate enormous premiums. In Chapter 3, 'Power x Compute,' we analyze how limited computing resources will become high-value-added under grid constraints. The 'physical bottlenecks' detailed in Chapter 6 are the biggest entry barriers in the inference economy, and the technologies that master this area will guarantee the true asset value of the 'unmarketed technologies' covered in Chapter 2.
This book presents a concrete roadmap for how physical field assets are integrated as digital APIs (Chapter 4) and how geopolitical regulations are converted into economic gains (Chapter 7). Heading toward 2030, where should capital be invested? The criteria for judgment are no longer the superiority of software, but whether the cold physical layer bottlenecks can be converted into 'profit.'
📖 Table of Contents Summary
Chapter 1: Executive Digest
1. Investment phases and profit sensitivity
2. Matching regional strategies with demand causality
3. Bargaining power and pricing authority within the value chain
4. Technical barriers: Differentiation in heat countermeasures and hedge structures
5. Execution framework: Capital allocation optimization and divestment criteria
6. Conclusion: Why Now? (Structural proof of waiting costs)
Chapter 2: Asset Value of Unmarketed Technologies - Quantifying Potential Gains
1. Scoring 'signs' and monitoring systems
2. Redefining cost structure and 'residual value' through R&D autonomy
3. Remaining 'physical filling' and regional roadmaps
4. Deciphering investment 'wait time': PLT (Physical Lead Time) model
Chapter 3: Power x Compute - Premiumization under Grid Constraints
1. Rising power unit prices and the economics of 'computation sparsity'
2. End-to-End evaluation of 1500VDC systems and next-gen power devices
3. Revenue model for integrated power and computation (Power-to-Inference)
4. Investment phases by time axis and risk symmetry matrix
Chapter 4: Physical AI and Spatial Intelligence - API-izing Field Assets
1. Digital twinning of physical assets: Interface layer turning equipment into 'computing resources'
2. Commercialization scenarios for mobile intelligence: From remote control to full autonomy - The scramble for the labor replacement market
Chapter 5: Autonomy and Minimizing External Dependency - Redefining the Supply Chain