[Related Articles…]The Advent of the AI Agent Economy — Key Points SummaryAnalysis of Digital Transformation and Corporate Productivity Innovation Cases

*Source: [ 티타임즈TV ]

● Korea’s Genius Youth, Fragile Minds- Economic Alarm

The Current State of Korean Youth Acting Increasingly Childish Despite Being Smarter, and an Interpretation from the Perspective of Global Economic Outlook and AI Trends

Key contents covered in this article:

• Developmental and upbringing factors that lead to increased mental health vulnerability among young people despite their increased intelligence.
• Practical diagnostic signals to distinguish between burnout and depression, and concrete methods for recovery without medication.
• Core insight not often covered in other articles: The structural impact of the combination of high cognitive ability and low resilience on the labor market and productivity.
• Practical scenarios of how the global economic outlook (labor supply, productivity) and AI trends (automation, personalized treatment) interact with youth mental health.
• Actionable intervention plans that can be immediately applied at the parental, corporate, and policy levels.

1) Chronological Summary: Key Issues at Each Stage – Developmental → Adolescence → Early Adulthood (Employment)

Childhood (Early Education & Parenting Patterns)

• Excessive early education and competition-focused parenting inhibit the development of autonomy and self-directedness.
• Result: Failure in separation/individuation, delayed identity formation.
• Connection to Economy & AI Keywords: High cognitive achievement but weakened metacognition and problem-solving abilities, leading to a risk of reduced adaptability in future AI-based job transitions.

Adolescence (Sociality & Resilience Formation)

• Opportunities for developing social skills and resilience through peer relationships and failure experiences are diminished.
• Result: Inability to recover from setbacks, continuation of adolescent behavioral patterns.

Early Adulthood (University & First Job)

• Importance of “the first step”: The first job or academic background exerts excessive influence on social stigma and identity.
• Result: Collapse of self-esteem, deepening of lethargy and burnout in case of job loss or evaluation failure.
• Economic Impact: Combined with youth unemployment and non-employment issues, leading to decreased labor participation and productivity loss.

2) Burnout vs. Depression: On-the-Spot Distinction Criteria and Self-Diagnosis Hints

Main Distinction Points

• Duration and Functional Impairment: The key is how long symptoms persist (weeks to months) and the degree of impairment in daily life and job functions.
• Burnout: Primarily focuses on job-related feelings of powerlessness, cynicism, and decreased task performance.
• Depression: Includes overall functional impairment such as low mood, feelings of worthlessness, changes in sleep/appetite, and suicidal thoughts.

Self-Diagnosis Checklist (Practical for On-Site Use)

• Ability to maintain daily functions (commuting, schooling, social relationships) in the past two weeks.
• Presence of extreme changes in sleep/appetite.
• Presence of recurrent feelings of despair or suicidal thoughts.
• Possibility of recovery from job-related stress (Do you recover after a day or two of rest?).

Emergency Signals (Professional Intervention Recommended)

• Suicidal thoughts, severe lethargy making daily life impossible, or loss of daily function due to panic attacks require immediate consultation with a specialist.

3) The Best Practices for Recovery Without Medication (Prioritized for On-Site Application)

Priority 1: Establish a Regular Routine

• Fix consistent wake-up, sleep, and meal times.
• Repeat small experiences of achievement by setting short-term goals (3 days, 1 week).

Priority 2: Behavioral Activation (Graded Exposure)

• Divide and perform small, clear tasks to accumulate a sense of completion.
• Intentionally repeat experiences of safely recovering from failure through “learning to fall” training.

Priority 3: Metacognition + Growth Mindset Training

• Objectively analyze failures and weaknesses to devise improvement strategies.
• Approach with “Let’s change the method” rather than “I can’t do it.”

Priority 4: Strengthen Social Connections and Support Networks

• Structured conversations with parents, colleagues, and mentors (including family meetings).
• Utilize EAP (Employee Assistance Programs) within companies / local communities.

Priority 5: Physical Activity, Sleep, Nutrition

• Regular exercise (3+ times a week), sleep hygiene, reducing excessive sugar intake.

4) 7 Things Parents, Education, and Businesses Must Change Immediately (Actionable Steps)

Parents (Home)

• Teach “how to fall safely and recover” by allowing failure.
• Assign tasks focused on cooperation and problem-solving instead of competition.

Schools & Education

• Include metacognition and problem-solving education in the curriculum.
• Reflect resilience and collaboration skills in evaluation systems.

Businesses (Organizations)

• Include resilience training in initial adaptation programs for new employees.
• Prevent burnout through psychological safety at work, in-house counseling (EAP), and flexible working arrangements.

Policy & Society

• Expand mental health accessibility: Financial support for community-based non-pharmacological treatments (psychotherapy, group therapy).
• Link youth retraining and transition programs with mental health support (including psychological counseling).

5) The Most Important Content Not Often Mentioned by Other Media (Original Insights)

The ‘Productivity Trap’ of High Cognitive Ability + Low Resilience

• Highly educated and highly qualified young people often have strong complex problem-solving abilities but low resilience due to limited experience with failure.
• This combination yields high productivity in the short term, but leads to a sharp decline in productivity and withdrawal in situations of repeated frustration (e.g., first job failure, project setback).
• Economic Effect: Increased volatility in labor input, higher recruitment and training costs, and increased human resource risk for companies.

Cross-Impact with AI Trends (Opportunities and Threats)

• Opportunities: Rapid reskilling possible through AI training and personalized learning.
• Threats: Psychological instability amplified (collapse of professional identity) as automation eliminates simple, repetitive tasks.
• Key Point: To reduce social costs, the pace of nurturing young people’s psychological adaptability must exceed the pace at which AI replaces jobs.

Data & Algorithm Issues

• AI-based mental health services can offer personalized support but may suffer from trust degradation due to privacy and bias issues.
• Solution: Transparent algorithms and hybrid models with medical professionals are recommended.

6) Global Economic Outlook (Reflecting Global Economic Outlook Keywords) and Macro Impact of Youth Mental Health

Labor Supply Aspect

• Combined with low birth rates and delayed marriages, reduced labor participation among young people poses a downside risk to economic growth.
• Worsening mental health directly impacts labor participation and productivity.

Productivity & Innovation Aspect

• The departure of short-term highly educated personnel can lead to a slowdown in innovation in technology-intensive industries.
• As job structures transform due to AI trends, a lack of psychological adaptability increases transition costs.

Policy Implications

• Financial and educational investment should be concurrently directed at ‘skills’ and ‘psychological resilience’ for a higher ROI.
• Joint investment by businesses and government (retraining + mental health support) is essential for maintaining global competitiveness.

7) Practical Recommendations from an AI Trend Perspective

Individual Level

• Incorporate metacognition and problem-solving practice into daily life using AI-based adaptive learning platforms.
• Utilize digital therapeutics (apps, chatbots) as supplementary tools, while also undergoing periodic professional supervision.

Corporate Level

• Include ‘resilience indicators’ and ‘learning flexibility’ as evaluation criteria in recruitment.
• Use AI tools to identify employee psychological states in real-time but disclose transparent privacy protection policies.

Policy Level

• Integrate AI-powered mental health early screening and management into the public healthcare system.
• Provide AI-based customized pathways in retraining programs to lower job transition costs.

8) Practical Checklist: 10-Minute Action Plan for Individuals, Parents, Businesses, and Policy

Individual (10 minutes)

• Break down one task from today’s schedule into 10-minute units and try to execute it.
• Write down a date in the calendar to try again if you fail.

Parents (10 minutes)

• Ask your child about a recent failure experience and explain “how to fall safely.”

Business (10 minutes)

• Add a phrase related to ‘learning flexibility’ or ‘resilience’ to a job posting.

Policy (10 minutes)

• Discuss with local health centers to propose a pilot project for non-pharmacological mental health workshops.

9) Warning Signs and Recommendation for Professional Consultation (On-Site Response Guide)

Immediate Consultation Recommended

• Suicidal thoughts, complete functional impairment for an entire day, recurrent panic attacks.

Priority Consultation Consideration

• Daily functional impairment lasting for more than 2 weeks, extreme changes in sleep/appetite, social isolation.

Treatment Selection Guide

• Medication is effective when functional impairment is severe.
• For mild to moderate symptoms, prioritize CBT, behavioral activation, exercise, and sleep improvement.

< Summary >

• Causes: Excessive early education and overprotection hinder the development of autonomy and resilience, increasing mental health vulnerability among youth.
• Diagnosis: Burnout and depression are distinguished by duration and functional impairment; use a self-diagnosis checklist to identify emergency signals.
• Recovery Methods: Regular routines, behavioral activation, metacognition training, social support, and physical activity are key.
• Original Insight: High cognitive ability + low resilience can become a ‘trap’ for the labor market and productivity.
• Economy & AI Link: Mental health issues have a real impact on labor supply and productivity; AI offers opportunities but poses risks without adaptability.
• Implementation: Concurrent investment in resilience education and psychological support systems by parents, schools, businesses, and policies yields a better cost-benefit ratio.

[Related Articles…]

• Youth Unemployment and the Future: Changes and Strategies in the Job Market (Summary)
• AI Changing the Labor Market: How Korean Youth Can Respond (Summary)nextgeninsight.net/

*Source: [ 지식인사이드 ]

● AI’s Efficiency Bomb Baidu A3B, K2 Think Decimate Scale, Remake Markets

A New Phase: The ‘Efficiency-Centric’ AI Revolution Revealed by Baidu A3B and MBZUAI K2 Think — Key Contents Covered in This Article

Key technological points that will change the landscape of AI and artificial intelligence (3B active parameter MoE, 128K context, verifiable rewards, agent-like inference),The true meaning often overlooked by other media (router regularization, token balance loss, gradual rotary embedding training schedules, etc.),Practical strategies that businesses, investors, and developers must immediately adopt (on-premise/edge deployment, cost per unit calculation, methods for utilizing verifiable rewards),Including an analysis of economic impact and regulatory/security risks.Reading this will allow you to grasp not only the technical differences between A3B and K2 Think, but also why ‘parameter count’ is no longer the core metric, and how this change reshapes business and economic outlooks, all at once.

Announcement Overview: Key Messages from the Two Models (Chronological Order: Announcement → Design → Training → Inference → Deployment)

Baidu ERNIE-4.5-21B A3B Key Contents: Mixture of Experts (MoE) architecture, 21B total parameters but only 3B active per token, 128K context window, Apache-2.0 open source, built-in tool calling (Structured function calling).MBZUAI K2 Think Key Contents: Stepwise Chain-of-Thought (LoT) supervised tuning based on a 32B backbone, verifiable rewards (Guru dataset 92K), agent-like planning (plan → candidate solutions → verification), 2,000 tokens/s inference speed, full stack open source.Common Message: “Smart design and training pipelines can end the simple race for scale.”

A3B (ERNIE-4.5-21B) — Technical Points of Design and Training

MoE Design and Active Parameter StrategyA3B uses an MoE architecture with a total of 21B parameters, but the router selects experts to activate for each token, allowing only about 3B to operate per token.This approach reduces inference and training costs while retaining the ‘specialization’ benefits of each expert.

Router Stabilization: Orthogonalization Loss and Token-Balanced LossTo prevent the router from over-assigning tokens to specific experts, router regularization (orthogonalization loss) and token balance loss were applied.Without this, MoE experts can collapse, making it difficult to achieve actual efficiency — this is a crucial point often overlooked in most reports.

Achieving Long Context (128K)The model directly learned long contexts by gradually scaling up rotary position embedding during training (10K → 500K).This method is fundamentally more stable than other workarounds that ‘solve long contexts simply with a sliding window’.

Tool Usage and Deployment FriendlinessA3B inherently supports structured function calling, allowing it to directly invoke external APIs, calculators, or search tools during inference.This is a significant advantage that can be directly integrated into enterprise RAG (Retrieval-Augmented Generation) and multi-agent workflows.

Training Pipeline (Stepwise)1) Text Pre-training: Gradual increase in token length (8K → 128K).2) Supervised Tuning: Specialized in mathematics, logic, coding, and science.3) Progressive Reinforcement Learning: Logic → Mathematics → Programming → Comprehensive Reasoning.Additionally, unified preference optimization was introduced to mitigate reward hacking during the alignment phase.

K2 Think — Differentiation Created by ‘Inference Architecture’ and Verifiable Rewards

Chain-of-Thought Supervised Tuning (LoT)In the early stages, K2 extensively learned from examples solved step-by-step by humans, embedding ‘stepwise thinking’ within the model.As a result, the quality of mathematics and coding significantly improved from the initial training rounds.

Verifiable Rewards (Guru 92K)K2’s major innovation is designing rewards based on ‘verifiable’ correct answers.By using objectively verifiable data as rewards, rather than simple human preference, the reinforcement learning signal becomes much more reliable.This approach structurally reduces hallucination and reward hacking issues.

Inference Time ‘Plan → Candidate Generation → Verification’ PipelineWhen generating a response, K2 first outlines a simple plan, then generates multiple candidate answers, and finally verifies each candidate.This process makes responses shorter and more accurate, simultaneously reducing error rates and verbosity.

Speed and Hardware OptimizationInference achieves 2,000 tokens/s performance through speculative decoding (multi-token prediction technique) and large-scale inference hardware like Cerebras wafer-scale systems.The fact that ‘throughput from a productivity perspective’ is achievable in a practical environment is significant.

The Power of Full Open SourceK2 released weights, data, and deployment code, lowering reproducibility and enterprise adoption barriers.This has significant implications for both the research ecosystem and enterprise IT strategies.

Benchmarks and Real-World Performance (Important: Look at ‘Response Length’ and ‘Verifiability’ as much as the numbers)

It showed results competing with or surpassing existing large models in areas like mathematics, coding, and science.Examples: AIME24 90.83 (response shortened by 6.7%), AIM25 81.24 (shortened by 3.9%), HMMT25 73.75 (shortened by 7.2%).In coding Live Codebench V5, K2 achieved 63.97, outperforming larger models, and its response length was also more than 10% shorter on average.Key: Not just raw scores, but producing ‘short and verifiable’ answers creates greater value in practical applications.

Crucial Points Often Undiscussed by Other Media (This is Where Strategies Change)

Importance of Router Regularization and Token BalanceFor MoE to function effectively, the router must distribute tokens ‘equitably’.Many analyses focus solely on parameter count, but these router losses are actually central to model performance and stability.

Truly Long Context is a Training Schedule ProblemA large context window like 128K isn’t achieved by simply increasing embedding length.Gradual token length increase combined with memory and attention scheduling is necessary, and the public release of this training recipe is A3B’s strength.

Practical Implications of Verifiable RewardsVerifiable rewards are key to reducing human review costs in ‘automated systems’.At an enterprise level, it’s difficult to trust and automate models without reward signals that allow ‘checking the results’.

Inference Pipeline Design Creates ‘Short and Accurate’ AnswersK2’s plan→generate→verify structure transforms the inference process itself into a quality assurance system.This goes beyond simply meaning the model answers well; it reduces ‘adoption costs (review and correction time)’.

Economic and Industrial Implications (Macro and Micro Perspectives)

Enterprise IT and Cloud Business RestructuringEfficient models promote on-premise and edge deployment, reducing reliance on cloud APIs.This could put structural pressure on the text API business models of AWS, GCP, and Azure.

Startup and Product Strategy ShiftsUsing smaller, more efficient models lowers monthly inference costs, making it easier to validate business models.Commercialization is rapidly achievable, especially in B2B vertical applications (legal document summarization, clinical research assistance, financial report generation).

Labor Market and Productivity ImpactIn the long term, the automation of knowledge work will accelerate, increasing productivity, but demand for some repetitive professional roles may decrease.However, automation based on ‘verifiable rewards’ will reduce the risk of malfunctions, accelerating corporate adoption.

Investment PerspectiveInvestors should now look beyond simple parameter count to inference cost per token, throughput, verifiability, and open-license status.The investment value in hardware-software integration (e.g., specialized hardware like Cerebras) is also subject to re-evaluation.

Practical Application Guide — 8 Things Businesses/Developers Must Do Immediately

1) Shift your model selection criteria from ‘parameter count’ to ‘inference cost per unit, context, and verifiability’.2) For PoCs, use open-source models like A3B or K2 to actually measure cost and performance.3) For long document processing (contracts, research), include 128K-level context in your test cases.4) In product design, standardize on tool calling interfaces (Structured function calling) to facilitate workflow integration.5) Create your own verifiable reward data and connect it to your RL pipeline (especially advantageous for companies with domain knowledge).6) In infrastructure planning, consider inference throughput (tokens/s) and hardware integration (e.g., Cerebras, A100, etc.).7) Design output verification and log/evidence preservation (policy) for security and regulatory issues.8) Workforce retraining: Educate product managers, legal/compliance officers on the principles of ‘verifiable AI’.

Investment and Policy Recommendations

To Investors: Make model efficiency (throughput, cost/token) and open-license status your core evaluation metrics.To Governments/Regulatory Authorities: The proliferation of open-source models is both an opportunity and a risk for regional AI competitiveness. Regulations should be re-aligned around transparency and accountability standards.To Corporate Executives: Allocate R&D budgets to inference pipelines, verifiable data acquisition, and infrastructure rather than simply model expansion.

Risks and Response Strategies

Forgery, Misuse, and Security RisksThe more open models become, the greater the risk of misuse. Companies must mandatorily implement internal usage guidelines and output verification systems.

Regulatory and Legal Liability IssuesVerifiable rewards help provide explainable grounds for ‘why this answer was given’ in legal disputes.Therefore, include evidence preservation logs (generated plans, candidates, verification results) during the design phase.

Sustainable CompetitivenessIf open models become standard, competitive advantage shifts to ‘data, domain verification pipelines, and service integration’.Failure to invest in these areas will lead to being easily outcompeted.

Finally — Why This Event is Key from an ‘Economic Outlook (Economic Growth·Productivity)’ Perspective

AI’s contribution to productivity is determined not by model size, but by ‘applicability’.What A3B and K2 demonstrated is ‘cost-effectiveness,’ which is a decisive trigger for businesses to adopt AI on a large scale.Therefore, in the 2025-2027 economic outlook, AI-related productivity shocks are likely to materialize, centered on ‘efficiency rather than scale’.This will lead to accelerated automation across industries, shifts in labor market structures, and the emergence of new business models, directly influencing investment and policy directions.

< Summary >A3B and K2 Think have rendered the large-parameter race meaningless with ‘small but smart’ designs.Key technological points include MoE’s active parameter strategy, router regularization, gradual long-context training, verifiable rewards, the plan-candidate-verify pipeline during inference, and inference efficiency (throughput).Practically, model selection criteria should shift to ‘inference cost, context, and verifiability,’ prioritizing on-premise/edge deployment, verifiable data construction, and output verification design.Economically, productivity and automation impacts will occur with an ‘efficiency-centric’ focus, and the cloud ecosystem and investment strategies will be reshaped.

[Related Articles…]AI Investment Strategy: Analyzing Opportunities for Korean Startups in 2025Digital Transformation and Economic Outlook: How AI Will Change Korea’s Industrial Structure

*Source: [ AI Revolution ]

● Cybersecurity Shockwave Hacking Fuels Inflation, Cripples Supply Chains, Redefines Corporate Survival

Mastering Ethical Hacking: Roles, Techniques, Practical Processes, and 10 Key Insights Linked to Economic Risk

This article contains the following important information.It includes the actual distinction between Red vs Blue Teams and their roles within an organization, as well as an analysis of organizational structures like X-Force.It covers a hierarchical process from vulnerability scanning to penetration testing and Red Teaming (adversary simulation).It details the ‘pre-mortem’ that security architects must perform and methods for linking it to business risks.And it includes core points not often covered by YouTube or news—such as methods for measuring economic impact, the link between supply chain attacks and inflation, and how to quantify security investment ROI.Reading this article will help you immediately understand how ethical hacking is not just a technical exercise but is connected to managing a company’s global economic risks.

1) What is Ethical Hacking — Concept and Organizational Position

Ethical hacking is the activity of finding system weaknesses under authorized conditions.Ethical hackers perform a company’s ‘pre-mortem’ to proactively imagine system failure scenarios and design defenses.Ethical hacking goes beyond simply finding vulnerabilities; it evaluates business impacts (financial loss, reputational damage, regulatory risks).Particularly from a global economic perspective, cyber incidents can lead to supply chain disruptions and inflationary pressures, making security now a ‘macroeconomic risk,’ not just an ‘IT issue.’

2) Key Roles by Organizational Structure — Explained with the X-Force Model

IntelThey collect threat intelligence.They analyze dark web activities, malware trends, and attacker TTPs (Tactics, Techniques, and Procedures) to issue warnings to management and security teams.IR (Incident Response)This is the 911 role called upon during a breach.They are responsible for incident analysis, containment, eradication, recovery, and post-mortem.Red Team (Adversary Simulation)They simulate attacks on the organization from an attacker’s perspective.They reveal weaknesses in defenses through realistic threat scenarios.These three pillars circulate through intelligence → response → simulation, creating effective cybersecurity operations.

3) Practical Differences and Collaboration Methods of Red/Blue/Purple Teams

Red TeamThey act as authorized attackers.They mimic real attacker patterns to target the critical assets of the organization.Blue TeamThey operate intrusion detection, log analysis, and response systems.They are responsible for defense through SIEM, EDR, and network monitoring.Purple TeamThey integrate the results of Red and Blue teams and accelerate the learning cycle.They improve rules, signatures, and playbooks to ensure that techniques discovered by Red are detected by Blue.When the Purple Team functions well, the effectiveness (ROI) of security investments improves immediately.

4) Hierarchical Process of Ethical Hacking (Pyramid Model) — Organized by Time Flow

1) Vulnerability Scanning (Broadest Layer)Automated tools detect a wide range of vulnerabilities.This is a repetitive cycle of asset identification → scanning → reporting.2) Penetration Testing (Middle Layer)Experts target specific systems (web apps, authentication, infrastructure).It combines tools and manual efforts to assess actual exploitability and impact.3) Red Team/Adversary Simulation (Top Layer)They conduct long-term, multi-stage attacks targeting the entire organization.Attackers include social engineering, supply chain vulnerabilities, and zero-day emulation.Following this sequence ensures security maturity increases incrementally over time.

5) 7 Key Insights Not Often Discussed Elsewhere

1) Security needs to be redefined as a ‘business KPI’ to be effective.Translate cyber risks into terms like revenue loss, supply chain disruption days, or potential for inflation rise.2) Supply chain attacks are not merely regional infringements but cause ‘global economic’ shock.Disruptions to critical components or services can lead to supply chain bottlenecks, stimulating inflation.3) Making ‘pre-mortem’ an official process significantly reduces incident occurrence rates.From the design phase, ‘assume the system has already failed’ and work backward to find causes.4) MITRE ATT&CK should not be used as a simple metric alone.It must be combined with a business asset map to prioritize and align with attacker objectives.5) Automation is a ‘double-edged sword.’While vulnerability scanning and SIEM automation increase efficiency, final judgment should still be left to skilled human analysts.6) Security investment ROI must be quantified by detection rates, recovery time objectives (RTO), and cost savings to secure budget.7) Regulations and compliance should be approached not as a cost, but as part of enterprise-wide risk management.Regulatory violations lead to fines as well as a loss of trust and market value.

6) Technical Stack and Soft Skills Required in Practice

Hard SkillsNetwork protocols (TCP/IP), web security (OWASP), system/OS vulnerabilities, cryptography fundamentals.Tools: Burp Suite, Metasploit, Nmap, Wireshark, and SIEM (e.g., QRadar SIEM V7.5) operation capability.Programming: Python, Bash, PowerShell for writing automation and exploit scripts.Threat Intelligence Analysis: TTPs mapping, utilizing MITRE ATT&CK.Soft SkillsBusiness Communication: Ability to convey risks to non-technical stakeholders (executives).Collaboration and Coordination: Rapid cooperation with Red, Blue, and IR teams.Ethical Judgment: Understanding and adhering to legal and ethical boundaries.Even without strong programming skills, logical thinking and communication can create significant value.

7) Career Roadmap — From Entry-Level to Global Head in Chronological Order

Phase 1: Foundation (0-2 years)Gain network/system operations experience.Recommended to obtain basic security certifications (eJPT, CompTIA Security+, CEH, etc.).Phase 2: Intermediate (2-5 years)Practical experience in penetration testing, SIEM operations, and log analysis.Gain specialization with practical certifications like OSCP, Pentest+.Phase 3: Advanced (5-10 years)Lead Red Team operations, threat intelligence analysis, and adversary simulations.Expand organizational impact through Adversary Emulation and Purple Team activities.Phase 4: Leader/Strategy (10+ years)Perform roles like Global Head for strategy formulation and risk management.Make decisions at the intersection of business, regulation, finance, and security.A leader who propagates ‘hacker mindset’ within an organization creates more value than directly becoming a hacker.

8) Certifications and Training — Which Certifications Are Helpful in Practice?

Entry-level recommendations: eJPT, CompTIA Security+, CEH (foundational understanding).Core practical skills: OSCP (penetration testing practice), CRTP (Red Team/privilege escalation specialist), GNFA/GCIH (incident response).Operations/Analysis: SIEM-related certifications (e.g., IBM QRadar) and SOC Analyst tracks are greatly beneficial.Strategy/Management: CISSP, CISM can be essential choices for becoming a security leader.Certifications are not just passing marks; it’s important to demonstrate practical exercises and a portfolio (public reports, blogs, CTF results) alongside them.

9) Operational Execution Checklist

Start with asset inventory creation and prioritization.Apply MITRE ATT&CK based threat modeling.Include pre-mortem in the design phase.Combine regular Red Teaming (at least annually) with penetration testing (quarterly/based on asset criticality).Establish a Purple Team to shorten the learning cycle.Automate SIEM and EDR, but ensure periodic review by human analysts.Explain security investments with financial KPIs (cost savings, RTO reduction).

10) Importance of Ethical Hacking from an Economic Perspective — The Link Between Security and Finance

Cyber incidents extend beyond mere IT costs, leading to supply chain disruptions, which in turn result in increased production costs and inflationary pressures.Critical infrastructure breaches can create economic repercussions such as increased capital costs (heightened interest rate sensitivity) due to loss of trust.As digital transformation accelerates, cyber risks dictate a company’s competitiveness and global economic position.Therefore, security investment is not an expense but an ‘insurance’ that reduces macroeconomic risk, and ethical hackers are the ones who design the workings of that insurance.

< Summary >Ethical hacking is the activity of identifying and mitigating business risks through authorized attacks.Clarify roles using the X-Force model (Intel, IR, Red Team).A pyramid approach of vulnerability scanning → penetration testing → Red Teaming is effective.Pre-mortem and Purple Team are essential practical elements not often covered elsewhere.Security is directly linked to the global economy, supply chains, inflation, interest rates, and digital transformation, so it must be managed with KPIs from a management perspective.Build your career with practical experience, hands-on certifications (OSCP, QRadar/SOC related, etc.), and a portfolio.

[Related Articles…]The Connection Between Global Economic Risk and CybersecurityAnalyzing the Impact of Supply Chain Attacks on Inflation

*Source: [ IBM Technology ]

● 2-Minute AI App Build NanoBanana, Google AI’s Profit Secret

How to Create an AI Image Editing App in 2 Minutes — Nanobana + Google AI Studio Practical Guide

This guide contains content that allows you to follow along and build immediately, even if you only read the summary.It includes the 2-minute prototype creation sequence, actual prompt templates, methods for maintaining character consistency with Nanobana, cost and latency optimization techniques, and a copyright and safety checklist to review for commercialization.It especially delves into core points often not covered in other YouTube videos or news: the ‘image quality-cost tradeoff from a productization perspective’ and ‘reference set design for ensuring character consistency.’The content below is organized chronologically, enabling you to build a service MVP and conduct market validation from a startup perspective.

Prerequisites

A Google account and access to Google AI Studio are required.Prepare Nanobana-related images/datasets (5-10 sample images for reference).Prepare a representative image for the service (at least 1) and costume/style images to composite (transparent background PNGs if possible).Capturing simple UI screenshots (service screens to refer to) will make UI replication easier in the build.Pre-check basic personal data and copyright considerations (model consent, commercial use availability).Define your market target (e.g., Asian costumes, European fashion) considering global economic outlook and your product’s digital transformation strategy.From a startup perspective, pre-set KPIs to measure initial costs and productivity metrics.

Creating a 2-Minute Prototype with Google AI Studio Build (Practical Steps)

Step 1: Access Build and create a new project.Start a new build with the plus (+) icon and upload your reference UI screenshots.Write clear instructions in the description field.Example Prompt (simple, highly reproducible):”Implement the service UI shown in the attached image as is.Include image upload functionality (allowing 2 photos), dress compositing (original person’s photo + costume image), sliders for size/color/style adjustment, a generate button, and a download function.”Click Run to automatically generate code/files.Upload test images to the generated UI and click ‘Create Combination’ to check the results.This entire process, if prepared, can be completed in approximately 2 minutes (depending on network/image size).

Key Techniques for Using Nanobana — Tips Not Often Shared Elsewhere

Maintaining Character Consistency:Prepare 5-10 reference images with consistent lighting and similar poses.Consistently including ‘character name/features/color palette’ as metadata in the prompt reduces style drift.Provide few-shot examples: Increase reliability by showing 3-4 instances of A→B transformation for the same character.Copyright and Data Source Management:Document the sources of costumes and images used for training and compositing.If you plan commercialization, secure license checks and model (person) consent forms early.Quality-Cost Trade-off (Key):High-resolution realistic images significantly increase costs and latency.In the MVP stage, use medium resolution (512-1024px) + style correction to reduce costs by 2-5 times.Practical Tips:Limiting the compositing area with ControlNet or mask uploads makes natural compositing easier.When replicating a UI, upload a screenshot and instruct “Implement this UI as is” for the build to quickly generate basic components.

Detailed Implementation Steps by Demo

AI Character Dress-up App (Dress Compositing)Image Preparation: 1 person’s image + multiple dress images.Prompt: “Naturally composite the selected dress onto the original person’s photo.Provide UI for adjusting costume size and color tuning.”Implementation Tip: Use mask-based compositing to protect the face and hands, replacing only the clothing.UI: Upload → Select Dress → Preview → Download.

WorldDress AI (National Traditional Costumes)Data: Style guide images for each country (at least 3 representative images).Prompt: “Apply the traditional costume of the selected country to the uploaded person’s photo.Add style options (anime, 3D, realistic).”Localization: Essential to check cultural sensitivities for each country.

Dating Sim/Role-Playing Game (Interactive Image + Text)Composition: Reference set for maintaining character consistency + dialogue scenarios.Implementation: Chatbot engine (maintaining conversation state) + image generation trigger (generating images during specific events).Safety: Integrate age and sensuality guidelines into the system (including refusal guidelines).Practical Tip: Operating automatic background generation and costume changes as separate APIs makes performance optimization easier.

Commercialization Strategy & Business Model

Core Models: Subscription (SaaS), Pay-per-image, In-app purchases (costume/filter buying).B2B Opportunities: Automated product shots for e-commerce, detailed page image generation services for small businesses.B2C Opportunities: Personalized avatars, custom dating sim content sales.Monetization Tip: Free trial (a few generations) → encourage conversion to monthly subscription.Marketing: Drive virality with ‘traditional costume transformation’ campaigns on social media.Global Launch Strategy: Prioritize localization and partnerships tailored to cultural regulations in each country.All these processes must periodically re-evaluate market demand in line with global economic outlook and AI trends.

Cost and Infrastructure Optimization Tips (Operational Perspective)

Set cost guidelines by resolution (e.g., thumbnails free, medium resolution paid, high resolution premium).Generation Cache Strategy: Cache frequently generated combinations on a CDN to reduce costs and latency.Optimize costs by mixing on-demand and reserved instances.Latency Improvement: Display low-resolution previews on the frontend first, then generate high-resolution in the background.Monitoring: Real-time monitoring of metrics such as average cost per generation, success rate, and quality rating (user feedback).

Legal and Ethical Checklist (Must-Checks)

Verify consent for commercial use of portrait photos.Check licenses for datasets and costume images.Systematize filtering rules related to minors, sensuality, and hate speech.Pre-research country-specific regulations (e.g., face compositing regulations).Transparency: Recommended to display UI text notifying users that images have been composited.

Productization and Scaling Strategy

MVP: Quickly secure customer feedback with only core functionalities (upload → composite → download).A/B Testing: A/B test UI flows (upload method, preview location, payment inducement messages).Partnerships: Secure B2B channels by partnering with fashion brands and e-commerce platforms.Platform Expansion: Provide SDKs/APIs to allow other apps to reuse your technology.Customer Support: Automatically log generation failure cases and refusal reasons, responding with priority.

Market Positioning & Marketing Points

Differentiation Points: Maintaining character consistency and ‘locally culturally tailored style’ options.SEO and Content Strategy: Create blog and YouTube content using long-tail keywords such as ‘AI image editing’, ‘dating sim creation’, ‘how to use Nanobana’.Social Samples: Encourage User-Generated Content (UGC) to reduce viral marketing costs.Startup Growth Tactics: Target small business owners and creators as early customers for rapid validation.

< Summary >A 2-minute prototype is possible by inputting UI screenshots and a simple prompt into Google AI Studio Build.Maintaining character consistency with Nanobana requires 5-10 reference images and consistent prompt metadata.Key commercialization issues are copyright, model consent, and cultural sensitivity, which must be checked in advance.Cost optimization is resolved through a combination of resolution strategy, caching, and on-demand instances.The most crucial additional points are the ‘quality-cost trade-off from a product perspective’ and ‘reference design,’ which determine practical success.

[Related articles…]AI Studio Practical Guide: Summary for Rapid Service Creation with BuildNanobana Hackathon Analysis and Commercialization Strategy Summary

*Source: [ AI 겸임교수 이종범 ]

● ChatGPT Becomes Executor AI’s Combo Power Fuels Instant Economy, Market Upheaval

How will the market change if ChatGPT becomes an ‘Executor’? — Practical Opportunities and Global Economic Impact from This Week’s Major AI Updates (Including 7 Key Points)

The core content to be covered in this article is as follows:

The practical implications of ChatGPT becoming a conversational control center with OpenAI’s MCP tool support.

Why DeepAgent’s Stripe payment-integrated app creation will change how startups and freelancers monetize.

The structure and ROI variables of how Adobe’s enterprise AI agents actually increase revenue and operational efficiency.

Claude’s direct office file editing and its ripple effects on office automation, and potential Microsoft integration.

The impact of ByteDance Seedream/Cream 4.0’s performance and price competition on the image generation market and regulatory risks.

The ‘Combinatorial Effect’ resulting from these updates accumulating simultaneously, and the economic and policy challenges it brings, which are not widely covered by other YouTube/news outlets.

At the end of the article, we provide a short-term action plan and risk management checklist for businesses and individuals.

This article is organized around key keywords such as AI innovation, global economic outlook, digital transformation, productivity improvement, and automation.

1) OpenAI — ChatGPT Evolves into a ‘Conversation → Action’ Control Center (MCP Tool Support)

Feature Summary: With full support for MCP (Connector) tools in developer mode, ChatGPT can now not only make API calls but also execute read and write (modify) operations.

Technical Points: Activating ‘Developer Mode’ under Settings → Connectors → Advanced allows connection to your own MCP server, enabling real-time interaction via protocols like SSE and streaming HTTP.

Safety Mechanism: All tool calls show JSON input/output in advance, and ‘Pre-execution approval’ is the default, strengthening security control.

Work Application Examples: Jira ticket updates, direct CRM data modification, Zapier-like workflow triggers, automated code repository read → modify → commit.

Economic Significance: Transaction costs (task switching costs) within organizations will plummet as the boundaries of software usage disappear.

Key Risks: Tool permission and authentication errors, prompt injection leading to malfunction, data inconsistency due to incorrect source selection (multiple connectors for the same role).

Crucial Insight Not Well Addressed Elsewhere (Exclusive): Beyond simple ‘automation,’ the centralization of tool access strengthens the competitive advantage of platform owners.

As a result, it is highly likely to lead to data and task lock-in and increased platform dominance.

Policy Implications: Audit logs, per-tool permission separation, standard authentication (OAuth, etc.), and a governance framework are immediately needed.

2) ByteDance (Seedream/Cream 4.0) — The Speed and Price War of Image Generation

Product Core: Seedream/Cream 4.0 is a model that combines text-to-image generation and editing, claiming competitiveness against Gemini 2.5 (Nano Banana) by internal standards.

Price/Performance: Domestic pricing is similar to the previous generation, but it emphasizes a cost per image (approx. 0.03 cents) and 10x faster inference speed based on global hosting.

Market Significance: Faster and cheaper image generation will lower content production costs, leading to an explosive increase in image A/B testing for advertising, marketing, and e-commerce.

Regulatory Context: China has introduced copyright recognition and labeling requirements for AI-generated content, reinforcing the responsibility of providers and platforms.

Exclusive Insight: The low-cost nature of image models can lead to ‘content oversupply’ and will increase the costs for businesses and regulators to manage quality and authenticity (e.g., Deepfakes).

Corporate Strategy: Reference-image-based synthesis (allowing multiple references) competition will be a turning point for platform selection, and the image pipeline for advertisers and agencies will be reorganized.

3) DeepAgent — What a ‘Payment-Enabled App in 30 Minutes’ Means

Product Core: Generates a usable app with a single prompt, integrates Stripe payments with one click, and immediately connects to real-world revenue.

Technical Convenience: Automatically sets up keys, webhooks, and environment variables required for Stripe integration, and automatically generates checkout, email confirmation, and sales funnels.

Business Implications: With reduced initial development costs and time, the barrier to entrepreneurship shifts from technical implementation to ‘product-market fit (PMF)’.

Economic Ripple Effect: Freelancers, creators, and small businesses can quickly experiment with micro-monetization (micro-payments, subscriptions), leading to an increase in platform-based micro-businesses.

Structural Change Insight (What others don’t often mention): Payment-integrated generation tools dramatically increase the speed and number of ‘sellable ideas’, leading to more small-scale experiments than Silicon Valley-style large-scale development investments.

Scale/Competition: As success stories accumulate, an indie developer ecosystem will mature, and a new industry structure dependent on payment fees and platforms will emerge.

4) Adobe — Enterprise Agent Deployment in Practice (Experience Cloud)

Product Core: Adobe Experience Platform’s agent orchestrator interprets intent, mobilizes appropriate agents, and executes actions for marketing, customer journeys, and site optimization.

Adoption Status: Over 70% of existing customers use AI assistants, with major brands like Hershey and Lenovo included in application cases.

Technical Strength: A dynamic adaptive (reasoning) engine adjusts agent deployment in real-time according to changes in the journey.

Business Effect: Aims to improve short-term ROI through campaign personalization, automated experimentation, and data insight generation.

Governance Point: Agent tuning (Agent Composer, SDK, Registry) aligned with brand regulations, privacy, and compliance is essential.

Corporate Insight (Overlooked Point): Adobe’s agents are not just simple productivity tools but act as ‘automated revenue amplifiers’ for companies with existing CDP, CRM, and campaign assets.

5) Claude (Anthropic) — Reducing Office Labor Through Direct Office File Editing

Feature Summary: Allows uploading and batch editing of Word, Excel, PowerPoint, and PDF files with natural language commands, without opening them, while maintaining formatting (up to approx. 30MB limit).

Practical Examples: Batch currency conversion, standardization of job titles/labels, CSV to analysis report/chart generation, and full automation of repetitive tasks.

Integration Potential: Reports of integration with Microsoft (based on reports) would mean Claude acting as an ‘internal operator’ within Office365 if realized.

Productivity and Labor Market Impact: Automation of standardized document tasks will reduce high-frequency repetitive work, and that time will likely be reallocated to higher-value analytical and strategic tasks.

6) Combinatorial Effect (The Impact These Updates Have Simultaneously)

Key Argument: The simultaneous emergence of a ‘conversational control center (MCP) + instant payment app + enterprise agent + document editor + low-cost image generation’ elevates AI from a mere assistant to an active participant (Operator) in tasks.

Market Outcome: End-to-end automation becomes possible from front office (sales, marketing) to back office (payments, documents, workflows).

Economic Ripple Effect: Reduced transaction costs, accelerated product launch speed, lower startup costs → In the short term, positive impact on GDP growth due to increased productivity and improved corporate profits is expected.

However, Structural Risks: Deepened platform concentration, personal data/data monopolies, and labor market structural changes (partial reduction of middle management and clerical jobs) accompany these policy challenges.

Monetary/Financial Impact: The expansion of AI app ecosystems with embedded payments will increase micro-payments and the subscription economy, which could change consumption patterns and the competitive landscape of payment infrastructure (payment gateways, tokenization).

7) 8 Things Practitioners and Businesses Should Do Right Now (Prioritized)

1. Review Permissions/Access Management: Set up MCP/connector permission policies and audit logs.

2. Align Data Governance: Specify a ‘source of truth’ for each connected data source.

3. Payment/Legal Review: Pre-check legal, tax, and refund regulations when integrating payments like Stripe.

4. Establish File Automation Protocols: If adopting large-scale editing like Claude’s, establish principles for format/template protection.

5. Build Benchmarking/Monitoring Systems: Set KPIs related to image/text quality and cost to automate supplier comparisons.

6. Employee Reskilling/Upskilling Plan: Develop strategic transition training for roles where repetitive tasks are being replaced.

7. Business Model Experimentation: Rapidly conduct small-scale paid tests with tools like DeepAgent (PMF confirmation within 30-90 days).

8. Review Regulatory Compliance/Risk Insurance: Consider insurance and legal preparations for data breaches and malfunction risks.

The Most Important Point Not Well Covered by Other Media (Summary Type)

Each technological update is important, but true innovation occurs when they are ‘chained’ together.

That is, when one tool handles payments, another optimizes customer journeys, and yet another automatically edits documents, the process is completed, allowing small business ideas to be monetized immediately, accelerating market experimentation exponentially.

This change goes beyond technical automation and alters the way capital is allocated in industries, and if investors and regulators do not quickly readjust, winner-take-all scenarios could become entrenched.

Recommendations from a Policy/Investment Perspective

Policy Makers: Prioritize the introduction of interoperability standards between platforms and audit/transparency regulations.

Corporate Executives: Develop data and tool ownership strategies and integrate AI-generated revenue streams into business models.

Investors: Base investment decisions not only on short-term productivity metrics but also on modeling platform dependency and regulatory risks.

Individuals (Employees/Founders): Prioritize business design capabilities (product, marketing, operations) over code, and execute experiments quickly.

< Summary >

AI has moved from words to actions.

ChatGPT’s MCP support creates a real-time control center by connecting tools, DeepAgent accelerates startup speed with payment-integrated apps, and Adobe applies enterprise automation to practical work.

Claude’s file editing feature automates document tasks, and ByteDance’s low-cost, high-speed image model significantly reduces content costs.

The most important point is that ‘the combination of functions,’ not ‘individual functions,’ changes the market structure.

Therefore, permission management, data governance, upskilling, and regulatory preparation are key, and both companies and individuals need rapid experimentation and risk preparedness.

[Related Articles…]

The Global Investment Flow Driven by the Fusion of AI and Finance — Summary

Accelerated Enterprise AI Adoption and Changes in Job Structure — Key Insights

*Source: [ AI Revolution ]

● AI Hallucination Incentive Explodes, Job Market Plunges into AI-War, Edge LLM Emerges – Policy, Survival Mandate

Key Summary — What you’ll gain from this article: An experimental reinterpretation of ‘Why LLMs Hallucinate,’ a reality check on Dario Amodei’s ‘90% code writing’ prediction, the practical chaos and survival strategies created by the AI-driven job market, and new opportunities for Edge computing, preservation, and privacy enabled by business card-sized LLMs — including practical solutions and policy proposals not often covered by other news outlets.

Immediate (Current) — This Week’s Key Headlines Summary

Oracle’s large-scale infrastructure deals and the surge in data center construction are not just good news; they are re-accelerating the ‘regional concentration’ of AI infrastructure.

The iPhone 17 demonstrated ‘incremental improvements’ in consumer AI features; end-user perceived innovation and core model advancement remain separate.

OpenAI’s paper ‘Why language models hallucinate’ sharply pinpointed not only the technical causes of the problem but also incentive issues within the evaluation and reward systems.

Amodei’s prediction that “AI will write 90% of developer code within 3-6 months” was a bold frame that confused ‘possibility’ with ‘real-world application,’ and the reality is closer to ‘increased code productivity + demand for maintenance and orchestration.’

The job search and recruitment landscape has become far more opaque than in the past due to ‘AI vs. AI’ noise (automated application generation ↔ automated screening).

The business card-sized LLM demo brought the imagination of the ‘micro and edge LLM’ era into reality. However, the entire ecosystem operates on a ‘cloud vs. edge’ tradeoff.

Short Term (6 Months) — Why We Must Re-Read ‘Why LLMs Hallucinate’

Key Finding: Hallucinations arise not merely from ‘token prediction limitations’ but also from designed incentives within learning/evaluation/reward systems (primarily RLHF, etc.).

A benchmark-centric culture signals to models that “the more answers they provide, the higher their score.”

Consequently, models tend to prefer ‘probabilistically better guesses,’ and honestly expressing uncertainty (e.g., “I don’t know,” “neutral”) results in an evaluation penalty.

Therefore, an approach that merely increases model size and data will not reduce hallucinations.

Solution Direction (a point often missed by other news outlets): Evaluation and reward functions must be redesigned to deliberately adjust the balance between ‘accuracy ↔ utility (creativity of conjecture) ↔ honest expression of uncertainty.’

Practical Suggestion: In internal evaluations, abandon ‘correct/incorrect’ binary scoring and separately measure confidence scores, provision of evidence (supporting paragraphs), and the use of tools (RAG, search, API tools).

Mid-Term (6-24 Months) — Code Automation (Amodei’s Prediction) Re-evaluation: Automation vs. Augmentation

Dario’s 90% prediction was a statement that conflated ‘possibility’ with ‘societal adoption.’

Reality: Much repetitive and templated code is already automated, and now ‘code design, verification, orchestration, and maintenance’ are emerging as more valuable skill sets than ‘code generation.’

Key Point: The “quantity of code” and the “type of valuable code” are different.

Resilient Areas (not yet automated): Understanding complex database schemas, designing high-performance/low-latency systems, and developing regulation-specific, safety-critical, or domain-specialized algorithms still largely rely on human roles.

Practical Tip: Organizations should prioritize retraining roles from ‘programmer → AI orchestrator’ and invest in code quality, security, and test automation (test generation, verifiable change history).

Short to Mid-Term Job Market (Now~1 Year) — Structural Causes and Solutions for the ‘Job Market Hell’

Problem Summary: Job seekers are in a flood of AI-generated resumes and portfolios, while recruiters encounter AI-filtered candidates, leading to a collapse of mutual trust.

While the media usually only talks about ‘AI failing/passing,’ the more crucial point is the severance of the ‘chain of trust.’

Practical Solution (a key point often overlooked elsewhere): Standardize ‘evidence-based verification’ in the hiring process. For example, timestamped code commits, actual remote live coding sessions, automated reference checks (phone calls, documentation requests), etc.

Recommendation for Businesses: Require metadata (provenance) for AI-generated documents and avoid screening out candidates solely based on automated scores.

Recommendation for Job Seekers: Differentiate yourselves by restoring networks (recommendations), providing public work evidence (GitHub, blogs), and preparing for interviews (demonstrating the ability to articulate exactly what’s written on your resume).

Policy Ideas: Transparent automated hiring disclosure laws (mandating disclosure of AI involvement in recruitment), applicant identity and copyright verification standards, and mandatory source attribution for AI-generated content.

Mid to Long-Term (1-5 Years) — Practicality, Preservation, and Privacy of Edge LLMs (Business Card-Sized)

While business card-sized LLMs are fascinating as a demo, their actual commercialization requires compromises with ‘power, memory, security, and update issues.’

Two Scenarios: (1) A world where global broadband penetration leads to most processing being handled in the cloud, (2) A world where local edge models are essential due to connectivity, latency, and privacy demands — both are likely to coexist.

Real-world applications of Edge LLMs: Industrial sensor interpretation (ultra-low latency), privacy-sensitive environments (medical devices, financial branches), local language and cultural preservation (offline translators), and educational/financial services in low-connectivity areas.

Preservation Perspective (an implication often not covered by the media): While LLMs are useful for ‘knowledge compression and generation,’ the selection of training data carries the risk of erasing cultures and minority languages.

Policy and Practical Recommendation: Build dedicated datasets to preserve local languages and traditional knowledge, disclose LLM contribution history (data sources), and strengthen open community support for smaller models.

Evaluation, Regulation, and Business Strategy — Action Plan You Can Apply Right Now

Corporate Strategy (Immediate): Redesign model evaluation metrics. Separately evaluate accuracy, calibration, evidence, and tool-use.

HR Strategy (Immediate Application): Don’t immediately dismiss AI-generated applications; instead, request ‘verifiable work products.’ Make live sessions, contribution records, and portfolio verification standard.

Developers/Engineers: Study not only how to use code generation tools but also ‘verification, refactoring, and test automation’ skills. The ability to become a quality manager for AI-generated code will soon be a competitive edge.

Policy Recommendation (Suggested): Legislate transparency regarding automated hiring and content generation, AI evaluation and safety standards (especially in critical infrastructure), and public support programs for minority languages and local data.

Practical Checklist for Readers (Things to Do Now)

For Businesses: Add a routine to request proof for all LLM responses, and differentiate between ‘I don’t know’ and ‘conjecture’ in internal evaluations.

For Recruiters: Do not solely rely on AI scores for initial document screening. Make practical tests, references, and GitHub evidence mandatory.

For Job Seekers: Create project-based portfolios (GitHub, blogs, demo videos), and be prepared to speak about every detail written on your resume during interviews.

For Developers: View code generation tools as ‘productivity tools,’ but convert refactoring, testing, and security review into essential skills.

Most Important Insight (Points Not Often Covered by Other News)

The core of the hallucination problem is closer to ‘external incentives’ than the model’s ‘internal mechanisms.’

Benchmark and evaluation design shape model behavior, and that behavior leads to societal consequences for the entire system (hiring chaos, flawed automation, etc.).

Therefore, both technical solutions (model improvement) and institutional solutions (evaluation, transparency, policy) are needed simultaneously.

Furthermore, if ‘hallucinations as creative conjectures’ (pro-hallucination) and ‘dissemination of false information’ are judged by the same standard, there is a risk of cutting off useful innovations.

To summarize: Our goal is not ‘complete elimination of hallucinations,’ but rather to institutionalize ‘when to allow conjecture and when to prohibit it’ and incorporate this into product design.

< Summary >Key: LLM hallucinations are a result of not only token prediction limitations but also evaluation and reward incentives.Practical: Redesign evaluation metrics and separately measure evidence, confidence, and tool use.Code Prediction: 90% code automation is a ‘possibility,’ but the reality is ‘augmentation + demand for maintenance and orchestration.’Job Market: To resolve AI↔AI noise, standardize evidence-based hiring (live assignments, portfolios, references).Edge LLM: When connectivity, privacy, and latency are intertwined, the practicality of ultra-small models increases.Policy: Transparency in automated hiring and data sources, and public protection of minority languages and local data are urgent.

[Related Articles…]

AI’s Impact on Korea’s Job Landscape — Key Summary and Response Strategies

The Meaning of Surging Global Data Center Investments — Infrastructure Reshaping the Global Economic Landscape

*Source: [ IBM Technology ]