● K-Robotaxi Licenses, Data, Billions – Korea’s Final Stand for Tech Sovereignty

Kakao’s K-Robotaxi: Questions Raised and a Roadmap for Solutions — Key Insights Focused on Licenses, Data, and Funding

Three crucial points not covered by other YouTube channels or news:1) Licenses (taxi licenses, medallions) are virtually the ultimate factor determining the success or failure of robotaxi adoption.2) Ownership of map, sensor, and driving data (map data) determines technological leadership.3) More than simple deregulation, realistic politico-economic designs like ‘license compensation, equity linkage, and public data trusts’ are necessary.

1. Current Situation (Now) — The Reality and Significance of the K-Robotaxi Proposal

Kakao Mobility has signed an MOU with the National Taxi Federation.This agreement is Kakao’s strategy to gradually introduce robotaxis by utilizing licensed corporate taxi fleets.The core of Kakao’s strategy is ‘license maintenance and transition within the current system.’The Bank of Korea recently warned in a report that delays in autonomous driving (robotaxi) adoption in Korea could ultimately lead to reliance on foreign software.Practical meaning: Political, institutional, and data competitiveness are greater bottlenecks than technology.

2. Overseas Cases (Past → Present Trends) — Commercialization Trajectories and Implications from the US and China

United States (Waymo, etc.):Waymo began research in the 2010s → piloted in Phoenix in 2018, transitioning to limited driverless commercialization from 2020.The US managed initial opposition (e.g., medallion value decline) through policy tools like regulation, total fleet limits, and contributions, allowing commercialization.Implication: The government strikes a balance with ‘conditional permission + fleet management + contributions’ rather than outright prohibition.

China (Baidu, Pony.ai, etc.):China expanded rapidly on a city-by-city basis, with cases like Wuhan achieving breakeven points.The Chinese approach rapidly secured data and infrastructure through central and local policy drives and massive funding.Implication: Political momentum to secure data and testing scale can drastically accelerate the learning curve.

3. Korea’s Technological Capability and Reality (Current Level Assessment)

While technical capabilities (autonomous driving algorithms, sensor hardware) are at an applicable level, Korea significantly lags in commercialization experience, driving data, and high-definition maps (HD maps).The small scale of testing and limited operating hours and vehicle numbers slow down the accumulation of learning data.Domestic tech startups and automotive companies’ investment scale is significantly smaller compared to trillions of KRW in the US and China.Regulatory and political risks (after the TADA incident) are stifling investment and blocking opportunities for experimentation and expansion.

4. Key Risks (Points often missed by other media)

Data Ownership Competition:If map, driving, and camera data flow to foreign companies (or overseas clouds), the domestic mobility ecosystem will become subservient.Decline in License Asset Value:A sharp drop in personal taxi license (medallion) values would cause strong social resistance and political costs.Platform Centralization Risk:If Kakao rapidly transitions to robotaxis based on corporate taxis, market dominance and “Galapagosization” (a Korea-specific ecosystem) will deepen.Talent and Technology Drain:If testing and learning data are concentrated overseas, domestic tech startups may become ‘componentized.’

5. Seven Strategies to Protect Our Own Services and Sovereignty (Action-Oriented)

1) License Compensation and Transition (Designing Social Consensus).

• Provide public compensation (cash, equity, pension-style subsidies) to license holders.
• Ex: Purchase a portion of licenses or offer a low-cost option to acquire equity in taxi companies in exchange for license retirement.2) Establishment of National Map and Data Trust.
• Store HD maps and driving data in a national or public-private trust to secure sovereignty.
• Strictly enforce API and data usage conditions when collaborating with foreign companies.3) Phased Pilot Expansion (Local → Regional → Metropolitan).
• Start initial scale in local areas (taxi-deficient regions) and expand every 6-12 months.
• Accelerate algorithm advancement by securing early data.4) Funding: Public Matching Funds + Large Private Investment.
• The government provides early risk funds and subsidies to attract private investment.
• Goal: Form a data loop with thousands of vehicles within 2-3 years.5) Open Stack and Standard API Support.
• Create interoperable software standards among domestic companies to prevent dependency.6) Labor Transition Program.
• Ease resistance through transition, retraining, license buybacks, and strengthening social safety nets for drivers.7) International Cooperation (Joint R&D Model instead of Technology Adoption).
• Allow technology partnerships with Waymo or Pony.ai, but design core software IP for joint research and co-ownership.

6. Phased Roadmap (Timeline: 0-2 years, 3-5 years, 5+ years)

0-2 years (Preparation and Pilot):

• Legal framework overhaul (limited transport licenses, total fleet system, contribution model).
• Focus on local pilots (data acquisition), begin HD map construction.
• Design National Map and Data Trust and secure initial funding (public matching funds).

3-5 years (Expansion and Verification):

• Implement license compensation based on agreements with corporate taxis and Kakao.
• Expand pilots to major cities nationwide, secure tens of thousands of driving data.
• Enhance domestic stack and disclose safety metrics to build public trust.

5+ years: Commercialization and Export Preparation:

• Expand commercial services upon meeting safety standards.
• Pursue exports to the Asia region based on domestic platforms (nationally owned data and software).

7. Budget and Scale (Realistic Numerical Proposal)

Recommended initial national support: 2-3 trillion KRW (for license compensation, testing infrastructure, matching funds).Private investment attraction target (3 years): Over 0.5-1 trillion KRW.Pilot scale: Initial 100-500 vehicles (local focus), expand to over 2,000 vehicles after 1 year.Without these figures, the data gap will become irrecoverable.

8. Technical Priorities (From a Practical Engineer’s Perspective)

1) Secure high-definition maps (HD maps) and an automatic update system.2) Sensor fusion and software stability (achieving Level 4 safety standards).3) Advanced integration of simulator and actual driving data.4) Security and Privacy: Encryption of camera/LiDAR data and mandatory domestic storage.

9. Labor and Social Consensus Design (Politico-Economic Practical Plan)

Propose three options for taxi license holders:A) Immediate cash compensation (partial acquisition).B) Replacement with mobility company equity (long-term income dividend).C) Retraining and transition (robotaxi maintenance/operation staff, data monitors).The core of the policy is to ‘reduce losses and share a portion of future profits.’

10. Five Priority Actions Recommended to Decision-Makers

1) Legislate data sovereignty (domestic storage and public trust for map and driving data).2) Allocate budget for license compensation and transition, and initiate a social consensus process.3) Establish a public risk fund to promote private investment.4) Grant priority to local pilots for rapid data accumulation.5) Participate in international consortia for technology exchange and joint R&D.

< Summary >Korea’s lagging position in robotaxis is not merely a technical issue but a complex problem involving licenses, data, funding, and politics.Kakao’s K-robotaxi, with its ‘license-based’ strategy, has created a realistic entry point, but without data sovereignty and large-scale learning data, there’s a significant risk of foreign dependency.The solution involves simultaneously implementing institutional, financial, and technological measures such as license compensation, a national data trust, public matching funds, local pilots, and international joint R&D.Without this plan, the domestic mobility industry is likely to become ‘componentized’ and dependent on foreign software.

[Related Articles…]K-Robotaxi: Kakao’s Strategic Alliance and License Reinforcement PlanAutonomous Driving Data Infrastructure: HD Map and Driving Data Strategy and Sovereignty Acquisition

*Source: [ 티타임즈TV ]

– 카카오의 ‘K-로보택시’ 방안의 내용은? 어느 정도 수준인가?

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● Silent Exodus Avoidant Workers Reshape Labor, Threaten Economy – AI’s Digital Counterpunch

The True Inner Thoughts of People Who Prefer Being Alone — 7 Key Insights Read from an Organizational, Economic, and AI Trend Perspective

Key contents to be covered in this article:

• Psychological mechanisms of avoidant attachment and immediate signs in the workplace
• A 3-minute nightly reflection routine and conversation script individuals can immediately use
• A ‘practical 3-sentence’ leadership playbook leaders/HR lose out on if they don’t know it
• Economic impact of avoidant tendencies on the labor market, turnover rates, and productivity (including a global economic perspective)
• Solution opportunities created by AI trends and digital transformation (e.g., mental health/conversation training chatbots) — Focusing on points not often covered in other YouTube videos or news

1) Chronological Order: Problem Recognition — Why Have More People Come to Prefer Being Alone?

Childhood attachment insecurity (experiences of neglect/abandonment) becomes ingrained as adult avoidant behavioral patterns.Modern society’s excessive empathy and information overload accelerate ’emotional exhaustion,’ strengthening avoidant tendencies.Avoidance is not simple indifference but a ‘self-protection strategy’ — a choice to minimize hurt by preemptively escaping the fear of abandonment.Avoidant individuals use ‘time and distance’ as defensive weapons, creating repetitive patterns in relationships (flight → relationship weakening).

2) Core Psychological Mechanism — Avoidant Internal Dynamics and Misunderstandings

Avoidant individuals do not lack a desire for connection at all. Rather, a strong desire for connection coexists with memories of being ‘too hurt.’Cognitive advice (“That might not happen”) is largely ineffective — emotional experiences (repeated past abandonment) dominate judgment.When self-efficacy and self-esteem decrease, avoidance becomes even more rigid.Without training in conflict or confrontation, ‘conflict skills’ do not develop, leading to easier retreat.

3) Individual Recovery Roadmap (Practicable Steps in Chronological Order)

1) Short-term (Today~1 week): 3-minute nightly reflection routine — Before sleeping each night, record ‘the scene where you withdrew’ and ‘the action you desired’ for 3 minutes.2) Mid-term (1~3 months): Conversation practice (scriptwriting + role-play) — Practice expressing needs without blame (e.g., “What I wanted was…”).3) Long-term (6 months~1 year): Accumulating corrective experiences — Reconstructing the attachment system by experiencing repeated positive interactions with one trustworthy person.4) Checkpoint: When failing, switch from ‘just spending time’ to structured reflection (what was learned, how to speak next time).

4) Organizational/Workplace Perspective — Costs and Opportunities Left by Avoidance

When avoidant individuals increase in the workplace: Communication declines, unresolved conflicts accumulate, leading to high turnover and increased personnel replacement costs.Economic perspective: Decreased productivity and inefficient human resources lead to weakened corporate competitiveness, which can broadly reflect in the economic outlook and market forecast.New trend: Avoidant individuals prefer remote/asynchronous work, potentially leading to a concentration of labor force by tendency in specific occupations/industries (inducing changes in labor market structure).Practical loss for leaders/HR: Employees who ‘quietly leave’ depart before problems surface, delaying problem diagnosis.

5) “Practical 3 Sentences” for Leaders and Teams to Memorize (Core Conversation Formula Presented in the Video)

1) “What is important to us is this.” — Clarifies common goals.2) “This is important to me, and what is important to you?” — Brings mutual needs to the surface.3) “What can each of us do to address these needs?” — Connects to tangible actions.These 3 sentences are highly practical tools for blocking blame and shifting to structured collaboration.

6) Organizational Strategy and Policy Proposals (Recommendations Considering Economic & AI Trends)

Policy 1: Introduce a ‘micro-confirmation’ process — Provide small, repetitive confirmations to avoidant individuals to reinforce object permanence signals.Policy 2: Incorporate psychological safety metrics into KPIs — Measure the frequency and quality of employee voice, linking it to turnover rates.Policy 3: Support digital mental health tools — Offer AI-based conversation coaching (chatbot), emotion reflection apps, etc., as part of benefits through digital transformation.These policies contribute to improving a company’s long-term market competitiveness through human resource efficiency (from a global economy perspective).

7) AI Trends and Business Opportunities — Points Not Often Covered Elsewhere

AI-based ‘corrective experience’ replication: Customized conversation coaching, scenario-based role-playing, and real-time feedback chatbots can be an expanded version of avoidant training.Value of datafication: Employee reflection records and conversation training data (anonymized) can quantify the state of psychological safety within an organization and link it to market forecasts.New service opportunities: ‘Micro-confirmation’ alert systems, avoidant-specific mentoring matching platforms, enterprise attachment style diagnostic SaaS.Key differentiating point (not often covered by other news): AI should be a ‘support’ for emotion, not a ‘replacement.’ The human corrective experience (one trusted acceptor) is a value that cannot be replicated by AI, but if AI handles repetitive training and signal provision, it can significantly shorten the recovery curve.

8) Practical Checklist — Things to Do in 10 Minutes Immediately

Individual: Record tonight’s 3-minute reflection (scene of withdrawal + one sentence you’ll change).Partner: Practice saying, “What I wished for from you today was this,” just once.Leader: Open the next meeting with the sentence, “Our common goal is ~.”HR: Add ‘psychological safety metrics’ to employee surveys and propose an AI conversation training pilot.

9) Long-term Impact — Benefits for Individuals, Businesses, and the Economy

Individual level: Improved relationship quality and life stability through restored self-esteem and self-efficacy.Corporate level: Improved loyalty and productivity, reduced turnover costs, fostered innovation within the organization.Macro (national/global) level: Increased labor market efficiency and reduced mental health costs contribute to economic outlook stability.AI and digital transformation will be key infrastructures accelerating this transition.

< Summary >

• Avoidance is a self-protection strategy, not flight, and emotional experiences dominate judgment.
• Individuals can begin recovery with ‘3-minute nightly reflection’ + conversation practice.
• Leaders should practice the 3 sentences: “Common goal / Individual importance / Actionable steps.”
• Avoidant tendencies can impact remote work and job distribution, causing long-term repercussions for the labor market and global economy (relevant to global economy, economic outlook, market forecast).
• AI trends and digital transformation, utilized for conversation training and psychological safety tools, can complement ‘corrective experiences’ to simultaneously enhance organizational efficiency and individual recovery.

[Related Articles…]Summary of Workplace Transformation and HR Innovation by AIKey Summary of Global Economic Outlook and Labor Market Structure Changes

*Source: [ 지식인사이드 ]

– 혼자 있는 게 편하다는 사람들의 진짜 속마음ㅣ지식인초대석 EP.67 (박재연 소장 2부)

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● DeepMind AI Cracks Navier-Stokes – Industry, Finance Face Seismic Shift

DeepMind Solves 100-Year-Old Navier-Stokes Mystery — Key Takeaways from This Article: Mathematical Validation of New Singularities Discovered by AI, Technical Core of PINNs and Graph Neural Networks, Practical Economic Impact on Climate, Aviation, and Space Industries, and ‘Financial’ Ripple Effects on Investment, Policy, and Supply Chains Often Missed by Other News Outlets, along with a Timeline.

1) Overview of Events (Recent Developments over the Past Few Months)

DeepMind researchers have discovered new solutions and singularities in the Navier–Stokes system.They directly minimized the residuals of Partial Differential Equations (PDEs) by combining Graph Neural Networks (GNNs) and Physics-Informed Neural Networks (PINNs).Mathematicians from NYU, Brown, Stanford, and other institutions rigorously verified the solutions proposed by AI, confirming their mathematical validity.This result is crucial not merely because it accelerates simulations, but because it represents the discovery of ‘new solutions.’Consequently, the limitations of models in practical fields such as climate prediction, aerospace design, and astrophysics are likely to be redefined.

2) Technical Core — Points Often Missed by Other Media Outlets

Role of PINNs: While traditional machine learning approximates functions from data, PINNs directly incorporate the physical constraints of PDEs into the learning process, ensuring that solutions do not violate physical laws.Necessity of Graph Neural Networks: Representing continuous media (fluids) with irregular graph structures instead of grids allows for more precise capture of complex boundary conditions and multi-scale interactions.High-Precision Optimization: The research team reduced errors to machine precision levels using second-order optimizers and numerical precision tuning, capturing fine solutions that traditional numerical analysis often missed.Lambda and Instability Order: AI discovered a clear regularity pattern between the growth rate (lambda) of singularities and their ‘order’ of instability.Visualization and Diagnostic Tools: High-resolution visualization of the vorticity field enabled frame-by-frame tracking of instability manifestation.

3) Mathematical and Physical Significance

Verification of Singularity Existence: The solutions proposed by AI led to mathematical proofs, demonstrating a new research loop of ‘AI discovery → human verification.’Stability vs. Instability: The fact that most discovered singularities are unstable (very fine-tuned) reinforces the existing intuition that the possibility of ‘stable blowup’ in unbounded 3D Navier–Stokes remains limited.Rich Solution Landscape: The observed regularity in the lambda-instability graph suggests the systematic existence of additional, as-yet-unknown solutions.Integration of Theory and Computation: This work is the first to demonstrate that PINNs can function not merely as an approximation tool but as a ‘hypothesis generator.’

4) Practical Impact — Industry-Specific Details

Climate and Meteorology Industry (1-3 years)AI-powered fluid solutions can significantly reduce computational costs while increasing the local resolution of large-scale climate models.This translates into immediate economic value creation through more accurate storm and typhoon forecasts and improved insurance risk models.Changes in risk pricing within the reinsurance and weather derivatives markets are likely to readjust financial product structures and capital requirements.

Aviation and Mobility (2-5 years)Precise turbulence analysis directly leads to drag reduction and improved aviation fuel efficiency in aircraft design.The CFD (Computational Fluid Dynamics) software market has the potential to be reshaped from one dominated by traditional large CAE firms to one with emerging PINNs-based SaaS startups.Reduced testing costs and fewer prototypes will shorten aviation R&D cycles.

Energy, Marine, and Plant Industries (3-7 years)Increased accuracy in field flow analysis for wind power and offshore plant design will improve energy yield and safety.Faster prediction and control of fluid-related incidents (such as oil or gas leaks) will reduce risk costs.

Defense and Space (Short to Medium Term)Improved accuracy in aerodynamics, combustion, and plasma simulations will lead to performance enhancements in weapon and rocket systems.Reinterpretation of models in astrophysics (e.g., fluids around black holes) has the potential to translate fundamental scientific insights into industrial technology.

5) Economic and Financial Repercussions (Key Points Often Missed by Other Media)

CFD Market Reshaping and Investment Opportunities: There’s a high probability that the traditional license-based business model will rapidly shift towards SaaS and cloud-based subscription models.Increased Hardware Demand: The demand for HPC, GPU, specialized analog, and edge semiconductors for high-precision AI training will grow, benefiting semiconductor and cloud infrastructure providers.Talent and Education Demand: The surge in demand for interdisciplinary talent combining PDE and machine learning will expand the education and reskilling markets.Policy and Regulatory Risks: Slow development of safety and verification standards could create regulatory bottlenecks during commercialization, delaying investment returns.Geopolitical Competition: Scientific superiority directly translates into strategic advantage, and AI-driven breakthroughs in fundamental science will become a focal point in the technological hegemony struggle between nations.

6) Timeline and Recommended Actions by Phase

Immediately (0-12 months)Companies: Introduce PINNs/PDE-AI experiments as small-scale pilots into your R&D portfolio.Investors: Prepare a due diligence list for CFD, HPC, cloud, and AI-fundamental science startups.Policymakers: Expedite the establishment of verification frameworks and research ethics guidelines.

Short-Term (1-3 years)Companies: Create case studies demonstrating cost-time savings through PINNs integration with existing CAE workflows, and strengthen patent and IP strategies.Financial Sector: Update weather and reinsurance models with new solutions and review derivative pricing adjustment scenarios.

Medium-Term (3-7 years)Industry: Prepare for regulatory approval by incorporating PINNs-based verification procedures into design and operational standards (e.g., aviation certification, plant safety standards).National Strategy: Invest in research infrastructure (HPC, AI talent development) and international collaboration (verification networks among mathematicians and AI researchers).

Long-Term (7-15 years)Economic Structure: Accumulation of energy efficiency and transportation cost savings will contribute to industrial productivity.Society: Automated scientific discovery will trigger a redefinition of roles between academia and industry.

7) Risks and Governance — Essential Points to Consider

Verifiability Issue: Solutions proposed by AI must undergo rigorous mathematical and physical verification processes.Misuse Risk: Advanced fluid analysis technology, when combined with military and security applications, poses risks, necessitating international regulatory discussions.Concentration Risk: If the commercialization of fundamental scientific discoveries becomes concentrated among a few global big tech companies and specific nations, it could lead to supply chain and power distortion issues.Ethics and Transparency: Policy must regulate the level of research data and model disclosure to ensure scientific reproducibility.

8) Specific Checklist for Investors and Businesses

Technology EvaluationDemand performance comparison benchmarks between PINNs and existing CFD solvers.Verify the reproducibility and verification documentation status of the model.

Business ModelAssess SaaS transition potential, customer acquisition cost (CAC), and existing license renewal risks.Review the patent portfolio and the status of research collaboration agreements (university/research institute).

Risk ManagementPrepare for regulatory approval potential, military application possibilities, and data/model governance risks through scenario planning.

9) Evolution of Academia-Industry Collaboration Models

AI → Human Verification Loop: A repetitive structure where AI proposes solutions and mathematicians prove them will become a standard research process.Importance of Public Benchmarks: Open datasets and verification pipelines will become key assets for enhancing industry credibility.Academia-Industry-Government Joint Funding: Governments and industries need to create joint funds to provide long-term support for PINNs infrastructure and talent.

< Summary >AI (DeepMind) discovered new singularities in Navier-Stokes by combining graph neural networks and PINNs, which have been mathematically verified.The technical core lies in PINNs directly incorporating physical constraints into learning, graph representation, and high-precision optimization.In the short term, cost and time innovations are expected in climate prediction, aerospace design, and the CFD market.In the mid to long term, repercussions at the level of energy efficiency, financial risk models, and national strategy will occur.Establishing investment, policy, regulatory, and public verification infrastructures will determine competitive advantage and safety.

[Related Articles…]5 Scenarios for How AI Will Transform Financial Markets — Investor’s PerspectiveNew Paradigms in Climate Prediction and Their Economic Impact

*Source: [ AI Revolution ]

– AI Just Solved a 100 Year Old Million Dollar Science Mystery

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● AI Agents Unleash Mainframe Billions

AI Agents & Mainframe: LLM-based Mainframe Optimization System — Core Components, Real Scenarios, and Enterprise Value All in One Place

Key takeaways from reading this article:

• We will organize step-by-step how AI agents transform existing notification-based operations in mainframes into an ‘perceive → decide → act’ paradigm.
• We will explain the specific mechanism by which LLMs/agents interpret mainframe-specific data (e.g., Call Home, SMF, RMF) in real-time to redistribute workload and perform preventative maintenance.
• We will present methods for “capturing the economic value of agent-driven operations” and a “step-by-step roadmap for immediate field use,” topics rarely covered in other YouTube videos or news.
• It includes technical, organizational, and regulatory risks that arise during adoption, along with a design and governance checklist to control them.
• Finally, we clearly propose PoC methods that show quick results and expansion strategies.

1) Background — The Current State and Limitations of Mainframe Operations

Mainframes manage critical business operations, with stability and availability being paramount.
Existing operations are based on sensor/threshold (e.g., temperature, CPU usage) Call Home events, forming an ‘alert’-centric, manual response structure.
Because multiple sysplexes are managed independently, optimizing the entire environment is difficult, often leading to extreme measures like completely shutting down development/test environments.
This approach has low problem prediction capabilities and generates repetitive manual tasks for operators (system programmers, SREs).

2) The Change Brought by AI Agents — Automation of “Perceive → Decide → Act”

Unlike simple prediction models, AI agents perceive inputs (sensors, logs, context), make decisions (reflecting priorities, policies), and directly execute actions (reallocation, scheduling, automated report generation).
The division of roles between LLMs and agents is important here.
LLMs excel at interpreting natural language and unstructured data, and explaining policies, while structured data processing is handled by specialized models/tools, making a hybrid architecture realistic.

3) Core Components of an Agent — Memory (Context + Knowledge) → Tools → Action

Context (Persistent Memory)

• Business objectives: Minimizing downtime, preventing errors, managing average CPU usage—clear objectives must be continuously provided to the agent.
• Defines ‘what to optimize,’ including operational policies like policies, SLAs, and cost limits.

Knowledge (System Data)

• Uses both structured and unstructured data, including Call Home events, SMF (RMF) records, logs, performance metrics, and configurations (e.g., DFSMS).
• Must maintain the latest state through data pipelines and real-time streaming (or near real-time batch).

Tools (Modules)

• Summarization/Aggregation Model: Summarizes large volumes of SMF/RMF data to generate high-level signals.
• Problem Identification Agent: Prioritizes issues through anomaly detection + rule-based filtering.
• Execution/Recommendation Engine: Generates actual commands such as reallocation, resource scaling down/up, and maintenance schedule proposals.

Action/Feedback Loop

• Designs a feedback loop that allows the agent to self-correct (online learning, policy adjustment) by observing results before and after execution.

4) Time-Sequence-Based Real Operational Scenario

Case: Hardware Temperature Rise Alert -> Perceive

• Call Home event received.
• Agent queries context (business criticality of the system, current workload) and records of similar past incidents.

Decide

• A summarization model combines relevant metrics from SMF/logs (disk I/O, CPU spikes, transaction delays) to generate candidate causes.
• Assesses overall impact considering multiple sysplexes (e.g., customer transaction delay risk) to determine priority.

Act

• Recommendation: Advise not to completely shut down a specific dev/test instance, but to ‘partially reduce’ throughput by 30% and reschedule specific batch jobs.
• Automated execution: Offload workload to another sysplex or adjust I/O priority within allowed policy limits.
• Result monitoring: Verify performance recovery after changes and update policies/models.

Effect

• Reduction in unnecessary service interruptions.
• Saving operators’ manual labor time.
• More resources can be allocated to innovative tasks (e.g., new system design).

5) Enterprise & Economic Perspective — Why Invest

From a digital transformation perspective, mainframe automation is beyond a simple technical upgrade; it’s central to global competitiveness.
Increased productivity and reduced downtime relative to IT investment directly enhance corporate competitiveness.
Its value is especially significant in industries where service continuity directly translates to revenue and trust, such as finance, telecommunications, and public sectors.
Agent adoption not only reduces operational costs but also provides long-term ROI through the opportunity cost of personnel (shifting from manual tasks to high-value work).

6) Major Technical & Organizational Risks During Implementation and Control Strategies

Data Quality and Continuity

• Requires data lake/ETL design to resolve missing/delayed SMF/Call Home data issues.

Explainability

• Essential to provide decision rationale logging and interpretable explanations (why this action?) for regulatory/auditing compliance.

Security and Access Control

• Apply the principle of least privilege and retain audit logs when agents execute system commands.

Operational Reliability

• Gradually reduce human oversight by setting clear boundaries between ‘automated execution’ and ‘recommendation’ (pre-approval, conditional execution).

Cost and Licensing Model

• Requires calculating Total Cost of Ownership (TCO) by considering both LLM/agent operating costs and mainframe resource costs.

7) Step-by-Step Adoption Roadmap (Time-Ordered Checklist for Implementation)

Phase 1 (1–3 months) — Pilot Design & PoC

• Set target KPIs (e.g., reduction in downtime, savings in manual work hours).
• Implement Call Home → Summarization → Recommendation flow in a single sysplex.

Phase 2 (3–9 months) — Expansion and Validation

• Integrate additional data sources (SMF, RMF, logs).
• Introduce recommendation-based automation (limited execution).
• Implement Explainability and audit logging.

Phase 3 (9–18 months) — Enterprise-wide Application and Optimization

• Introduce workload orchestration across multiple sysplexes.
• Operate policy-based automation and continuous learning pipelines.

Operational Checkpoints

• KPI review (quarterly), cost-benefit re-evaluation, risk and compliance review.

8) The Most Important Insight Rarely Covered by Other Media

Agents are not about ‘problem detection’ but about ‘automating operational decision-making’.
That is, the core value lies in the opportunity cost savings achieved by deciding and executing “what to reduce, move, or fix.”
Many contents focus on the technology (LLM) itself, but the real value depends on how agents are linked to operational policies and economic KPIs (e.g., loss per downtime, IT investment payback period).
Furthermore, by introducing modular agents (summarization, identification, execution) incrementally, organizational resistance can be significantly lowered.
Finally, leveraging regulatory/audit requirements not as ‘constraints’ but as ‘evidence of trust’ becomes a strategic opportunity to build trust capital with customers and regulators.

9) Practical Recommended Priorities (for quick results)

1. Define core KPIs and policies first (clearly state what to optimize).
2. Execute a ‘summarization → recommendation’ PoC with SMF/Call Home integration first.
3. Introduce automated execution gradually (recommendation → conditional auto → full auto) to build trust.
4. Design audit and explanation logs from the start to manage regulatory risks.
5. Invest in operator retraining to transform system programmers from ‘overseers’ to ‘strategists’.

< Summary >AI agents transform mainframe operations from simple alert processing to an automated structure of ‘perceive → decide → act’.
The core components consist of memory based on context (business objectives) and knowledge (Call Home, SMF, etc.), divided into summarization, problem identification, and action modules.
Practical application proceeds in the order of PoC → expansion → enterprise-wide orchestration, and economic value comes from reducing downtime and operational costs, and converting personnel to higher-value work.
A point rarely covered by other media is the strategy of “automating operational decision-making itself through agents, and directly linking this to economic KPIs.”

[Related Articles…]Mainframe AI Agent Adoption Cases and Cost Saving StrategiesIT Investment Strategy and Practical ROI Guide in the Digital Transformation Era

*Source: [ IBM Technology ]

– AI Agents & Mainframe: Optimized Systems Powered by LLMs

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*Source: https://www.aitimes.com/news/articleView.html?idxno=202543

● Meta’s Ray-Ban AI-EMG Unveil ‘Mind-Control’ Gateway, Economic Tsunami, Privacy Crisis

The Impact of Meta’s ‘Ray-Ban Display’ and ‘Neural Band’ Announcement — Key Topics Covered in This Article: Product Specs, Price, Launch Date, Meaning of Live Demo Failure, Industrial Ripple Effect of EMG-based Control Technology, Impact of Smart Glasses on the Global Economy and AI Ecosystem (AI Trends), Supply Chain, Semiconductor, and Optics Industry Opportunities, Regulatory and Privacy Risks, Investment and Business Strategies. Systematically organized, focusing on ‘Meta’s Monetization Strategy (Advertising, Subscriptions, Data Tiering)’, ‘The Significance of EMG as an Intermediate Step Towards Brain-Computer Interfaces (BCI)’, and ‘The Real Reasons Smart Glasses Won’t Immediately Replace Smartphones’, which are often not thoroughly covered in other news outlets.

1) Key Summary in Chronological Order — Announcement → Product → Demo → Market Reaction

Meta’s Announcement (Connect Event)Meta unveiled the Ray-Ban Display and Neural Band at Connect on September 17 (local time).Mark Zuckerberg presented the vision of “Glasses = Personal Superintelligence.”

Product Specifications, Price, and LaunchRay-Ban Display: Small display inside the right lens, shows messages/directions/translations/Meta apps, built-in camera/speaker/microphone/AI assistant, cloud-connected.Launch: Available in stores from September 30, starting at $799.Neural Band: Captures hand movements and brain-related gesture signals via EMG (electromyography) to convert them into commands, 18-hour battery life, waterproof.Oakley Vanguard: Smart goggles for sports, $499, launching October 21.Existing Ray-Ban Line Upgrade: Double battery life, improved camera, priced at $379.

Demo and Initial ReactionsZuckerberg’s live call demo partially failed (connection issues), but the audience cheered.Experts: The price point (especially $799) is unlikely to lead to immediate mass adoption.Promo video leaks were present.

2) Product and Technology Analysis — Hardware and Software Perspectives

Display and OpticsSmall in-lens display likely based on ‘wafer-level optics’ or microLED/projection.Visibility, parallax, and outdoor brightness correction are key for commercialization.Partnerships with lens manufacturers (e.g., Luxottica/Essilor) and supply chain control are competitive advantages.

Sensors, Camera, and AudioCamera stabilization (image stabilization) combined with real-time computer vision allows for AR overlays.Microphone/speaker audio UX quality is crucial for call and assistant experiences.

Significance of Neural Band (EMG)EMG offers non-invasive input, low latency for gesture/intent recognition.Important: EMG has a better signal band and accuracy for hand movement recognition than direct brainwaves (EEG), but it differs from BCI at the level of ‘mind-only control.’EMG is an ‘intermediate step’ towards BCI commercialization—it can first provide non-invasive control experience to secure usability and economies of scale.

AI and Cloud IntegrationCascading from glasses: Local sensors → Edge inference (simple tasks) → Cloud (complex RAG/large models).Latency, bandwidth, and privacy policies are central to service design.

3) Economic Impact (Global Economy) — Demand, Supply, and Industrial Chain Reactions

Demand Forecasting and Price ElasticityThe $799 premium model targets early adopters and tech enthusiasts.First-year sales estimate (conservative): 1-3 million units (assuming low conversion rates relative to Meta’s platform user base).The smart glasses market is more likely to fragment certain ‘usage patterns’ rather than entirely replace smartphones (smartphones will remain the primary device).

Supply Chain and Industrial OpportunitiesIncreased demand for semiconductor (SoC, AI accelerators), display (microLED, LCOS), optics/lens, camera module, battery, and sensor (EMG electrodes) companies.Companies developing small-to-medium high-resolution displays and custom AI edge chips may benefit.Advertising, Content, and Commerce Ecosystem: AR advertising, location-based overlays, and real-time shopping recommendations could reshape advertising revenue.

Labor Productivity and Service Sector ChangesIncreased productivity for knowledge workers (hands-free, in-view delivery of key information).In medical and industrial settings, real-time checklists and remote instructions can reduce errors → improved industrial efficiency.However, social costs related to working hours and attention diversion also coexist.

4) AI Trend Perspective — Technological and Ecosystem Changes Implied by This Announcement

Expansion of Multimodal AgentsMultimodal agents integrating visual, auditory, and electromyographic information are becoming a reality.Agents gain more precise context by combining user gestures (Neural Band), vision (lens), and conversation (voice).

Evolution of Edge vs. Cloud and RAGReal-time UX requires edge inference, but complex queries use RAG (Retrieval-Augmented Generation).Competition among Google, OpenAI, and Meta in RAG/vector embedding strategies will accelerate.

Agent and Platform ControlMeta is attempting user lock-in by bundling hardware + software + platform (Meta apps).Developer API and third-party app policies will be a turning point for ecosystem growth.

Next-Generation Human Augmentation DirectionThe ‘personal superintelligence’ vision means personalized information filters and augmented decision support.This is a long-term trend that will change the value chain of cognitive labor.

5) Regulatory, Privacy, and Ethical Risks

Biometric Data and Sensitive InformationEMG, visual recordings, and conversation logs are biometric and behavioral data subject to regulation.Risks of application and enforcement under European (GDPR), US (FTC, state-level regulations), and Korean personal information laws exist.

Potential for Medical Device RegulationIf advertised for health or memory assistance functions, medical device regulations (approval, clinical trials) may apply.Manufacturers and platforms could face increased legal liability if they offer services mistaken for medical procedures.

Advertising, Surveillance, and Social ImpactContinuous visual feeds utilized for advertising and targeting raise concerns about privacy and a surveillance society.Policymakers are likely to consider rules for ‘intentional collection limitations’ and ‘data minimization.’

6) Investment and Business Strategy Points

Meta’s Monetization Path (Key Aspect Not Well-Covered in Other News)Profit limits from hardware sales; Meta’s essential goal is to expand ‘service revenue, advertising, and subscriptions’ through platform and data.Contextual data generated through glasses (attention, gaze, behavioral signals) can be converted into high-value targeted advertising and personalized services.Gesture signals provided by the Neural Band can increase commerce and UX conversion rates, leading to higher Average Revenue Per User (ARPU).

Supplier and Investment IdeasBeneficiary companies: Edge AI chips (derivative lines from Qualcomm/NVIDIA wearable chips), microdisplay manufacturers, optics/lens companies, EMG sensor/packaging companies, battery/power management solutions.Risks: Adoption speed/scale and price sensitivity of the end-product company (Meta).

Short-to-Mid Term Investment Strategy ProposalShort-term (6-18 months): Interest in component stocks related to sensors, cameras, and batteries in the supply chain.Mid-term (1-3 years): Focus on platforms and SaaS with AR content/advertising platforms or potential partnerships.Risk diversification: Recommended portfolio diversification against regulatory and adoption failure possibilities.

7) Consumer Adoption and Usability — Why It Won’t Immediately Replace Smartphones

Positioning IssuesGlasses face barriers in ‘wearability’ and ‘social acceptability.’Privacy (social resistance to camera-on status) and fashion issues delay adoption speed.

Differentiated Usage PatternsGlasses are advantageous for quick information checks, voice, and gesture control.Heavy input, long reading, and complex tasks will remain with smartphones and laptops for the time being.

Price and Service BundlesGiven the high price, a ‘value proposition (features, services)’ is necessary.Subscription services (cloud AI assistant, health analysis, professional tools) are crucial strategies to increase ARPU.

8) Call to Action for Businesses and Developers

Developers and Startups: Prepare apps optimized for multimodal UX first.Starting points: Instant information (navigation, translation, workflow assistance), fitness, remote assistants.Enterprises (Business Units): Build user trust while mitigating regulatory risks through data minimization and consent-based design.Policymakers: Proactively design guidelines for personal information and biometric data protection.

9) Final Analysis and Forecast (Policy, Investment, and Business Perspectives)

Short-Term (1 year)Increased product awareness, limited sales to early adopters.Immediate monetization from advertising and subscriptions will be minimal.

Mid-Term (2-4 years)Improved usability with enhancements in EMG and multimodal agents.Fragmented demand → expansion into specific vertical markets like health, fitness, and industrial use.Intensified competition between platforms (Apple → glasses / Google AR, etc.).

Long-Term (5 years+)Smart glasses and wearables will partially change the paradigm of knowledge work, creating a new service economy.Personalized AI assistants and real-time information interaction will reshape the advertising and commerce ecosystem.

Key Risk ChecklistPrivacy and regulatory issues (biometric data).Low adoption due to dissatisfaction with utility versus price.Hardware/software integration failures (repeated bugs, demo failures).

Practical Recommended Actions (for Businesses and Investors)Hardware companies: Diversify supply chains, secure edge chip and display roadmaps.App developers: Design gesture and gaze-based UX, and provide low-bandwidth modes.Investors: Diversify investments across components, materials, and AI platforms, monitor regulatory momentum.

< Summary >

Meta’s Ray-Ban Display and Neural Band herald the beginning of the ‘multimodal personal AI’ era with EMG-based control and in-lens displays.While initial mass adoption may be limited due to price, usability, and regulatory issues, monetization through advertising and subscriptions, along with ecosystem lock-in, are core strategies.EMG is a crucial intermediate step towards non-invasive BCI, creating opportunities in the semiconductor, display, optics, and sensor industries.Investment points include edge AI chips, displays, sensor supply chains, AR content, and advertising platforms, with a diversified strategy considering regulatory risks.Developers should prioritize designing multimodal UX and data-minimizing solutions.

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