● AI’s Confident Lies Economic Data Poisoned, Trust Is The New Premium

Here’s the real cause and solution for ‘GPT-5 hallucination (false responses)’ as revealed by OpenAI — and the economic and social impacts we’re overlooking

Key points covered in this article (what you need to know at first glance).1) Why AI makes “confident lies,” and the structural reasons why existing benchmarks have inadvertently fostered this.2) The hidden meaning behind the performance comparison between GPT-5 (or GPT-5 family) and GPT-4o — a chronological summary of the interaction between accuracy, error rates, and ‘abstention’.3) Practical application methods for OpenAI’s proposed changes in evaluation (metrics, reward structure) to be truly effective.4) How economic indicators (GDP estimation, consumer sentiment index, financial market signals, etc.) that we rely on from news, social media, advertisements, and transaction data can be contaminated — a ‘precise economic risk’ analysis rarely discussed elsewhere.5) A 9-point practical checklist (from the perspective of evaluation, operations, regulation, and revenue models) that businesses, policymakers, and developers should adopt immediately.Reading this article will provide a comprehensive understanding, from the fundamental reasons why AI makes confident errors, to practical and policy-level solutions for rectifying them.

1. Recent Issue Timeline — From OpenAI Research Publication to Present

• Research Publication (Current Point in Time)OpenAI unveiled the causes of ‘hallucination’ and issues with the evaluation system.It pointed out that existing benchmarks reward ‘guessing’.

• Internal Experiments (Step-by-step)Comparison of 04 Mini vs GPT5 Thinking Mini: While 04 Mini appears superior based solely on accuracy metrics, the situation reverses when error rates and abstention rates are included.The GPT5 family significantly reduced hallucinations by increasing ‘I don’t know’ declarations.

• External Independent Research (Ongoing Simultaneously)Reports from NewsGuard and others still indicate approximately 40% misinformation.Reports from Imperva and others estimate that more than half of web traffic is bot/automated traffic.

2. Why Models Make ‘Confident Lies’ — The Fundamental Mechanism

• The Difference Between Next-Word Prediction (the Essence of NLP) and ‘Facts’Large language models are inherently trained to predict the next word.Training data lacks factuality labels (True/False), ensuring ‘fluency’ but not guaranteeing factual judgment ability.

• The Incentive Problem of BenchmarksExisting leaderboards give high scores for maximizing the ‘number of correct answers’.Choosing a blank (abstaining) results in a score of 0, but sometimes there’s no partial reward even for being wrong, making ‘always answer’ advantageous.Result: The model adopts a ‘guessing strategy,’ which directly leads to hallucination.

• The Paradox of Model Size and OverconfidenceLarger models ‘remember’ more facts, but when they have partial knowledge, they are more likely to overconfidently output incorrect assertions.Smaller models are more likely to state they don’t know, sometimes resulting in fewer hallucinations.

3. GPT-5 vs GPT-4o — How to Properly Interpret Performance Metrics

• Surface Metric (Accuracy)GPT-4o showed high accuracy in some benchmarks, but this might be an illusion caused by a higher ‘response frequency’.

• Key Metrics (Combination of Error Rate, Abstention Rate, Accuracy)GPT5 Thinking Mini recorded an abstention rate of 52%, resulting in a sharp drop in its error rate.Ultimately, leaderboards that ‘only look at accuracy’ can create false winners.

• External Verification ResultsIndependent evaluations (e.g., NewsGuard) report that GPT models still show a high rate of falsehoods (around 40%).This signals that real-world trustworthiness remains low despite model improvements.

4. The Gist and Practice of OpenAI’s Proposed ‘Evaluation Improvement’

• Proposal SummaryIt proposes re-designing benchmarks by imposing larger penalties for incorrect answers and awarding partial points for ‘I don’t know’.

• Practical Application (For Developers and Businesses)1) Redesigning evaluation metrics: Introducing accuracy + abstention + calibrated penalty.2) Partial scoring rules: Assigning weights based on answer similarity and presence of evidence.3) In production systems, setting an uncertainty threshold — if confidence is low, switch to ‘output refusal’ or ‘source citation’.

• Example Metrics (Recommended)Truth-Weighted F1: Factuality-based weighted F1.Calibrated Recall: Recall adjusted to align confidence with actual correct answer ratio.

5. The Most Important Economic and Social Implications We Rarely Hear About in the News

• Risk of ‘Contamination’ of Economic Indicators (New Insight)If a large volume of AI-generated text distorts consumer sentiment, public opinion, and search traffic, then GDP estimates, consumer indicators, and financial market signals can be misinterpreted.Example: Demand forecasting models based on social media trends can overreact to bot/AI content, leading to incorrect inventory and investment decisions.

• Emergence of a ‘Trust Premium’ in the Advertising and Media MarketsRising ad prices for genuine (verified) content → trust becomes an economic value.Companies must defend advertising effectiveness through ‘trust certification’.

• Data Dependency Risk in Financial Markets and PolicyWhen central banks and fund managers utilize real-time data (search, social media, news), the risk of distorted data-driven decision-making increases.This can add noise to monetary policy and interest rate decisions.

• Restructuring of the Labor Market and ProductivityEmployment demand surges in content creation, information verification, and data monitoring sectors.Conversely, revenue models based on mass production of low-quality content are likely to decrease in value.

6. A 9-Point Checklist for Businesses and Policymakers to Implement Immediately

• Evaluation and Development (Product Perspective)1) Adopt benchmarks that reward ‘I don’t know’ choices during model evaluation.2) Mandate external evidence (retrieval) integration and citation.

• Operations and Verification (Operational Perspective)3) Apply ‘Human-in-the-loop’ verification for each service level — essential for finance, healthcare, and legal sectors.4) Operate with a confidence threshold: If confidence is low, automatically refuse output or ‘request verification’.

• Market and Revenue (Business Perspective)5) Introduce content trustworthiness labels (verified tag) to justify premium pricing.6) Incorporate an ‘AI-noise adjustment’ factor in advertising and marketing measurement.

• Regulation and Governance (Policy Perspective)7) Standardize data provenance and implement audit systems.8) Mandate independent third-party ‘accuracy audits’ — major models require regular verification.9) Revise liability regulations for damages caused by misinformation — clarify responsibility boundaries for platforms and model providers.

7. Technical Guide for Developers to Use Immediately (Practical Application Order)

1) Configure an evidence-based response system using Retrieval-Augmented Generation (RAG).2) Add a ‘confidence score’ to model outputs and clearly indicate low confidence in the UI.3) Abstention policy: If confidence < X%, return “I don’t know.”4) Assign weight if an evidence link is present; otherwise, automatically place in a ‘verification queue’.5) Periodic sampling audit: Verify the factuality of random samples → utilize as re-training data.

8. Long-Term Implications from Regulatory and Market Perspectives

• Increasing Importance of Trust InfrastructureFact-checking infrastructure (evidence layers, source chains) could become a new public good in the digital economy.

• Need for Economic Policy RedesignInstitutions operating data-driven policies (e.g., real-time consumption indicators) must create separate metrics to measure the level of AI contamination in data.

• Need for International CooperationAI-generated information transcends borders, simultaneously impacting public opinion and finance.International standards (source tagging, audit specifications) must be established.

9. Practical Tips for Individuals (Freelancers, Entrepreneurs) to Use Immediately

• Monetization PerspectiveWhen building AI-automated revenue models, incorporate a ‘trust layer’ as product value.Example: Separate ‘verified’ series from ‘generative draft’ series on content channels.

• Risk Management PerspectiveAlways ensure high-risk information (investment recommendations, medical advice) passes human review.Transparently disclose AI’s uncertainties to users.

10. Future Outlook — What Changes Will Come Over Time (Timeline-Based Prediction)

• Short-Term (6–12 months)Widespread discussions on reforming benchmarks and evaluation methods.Increase in services experimenting with ‘abstain’ rules at the product level.

• Mid-Term (1–3 years)Formation of true-false verification services and a trust premium market.Introduction of industry-specific standards for data contamination adjustment.

• Long-Term (3–5 years+)Economic indicators and financial models will mandatorily include AI noise correction.’Trust infrastructure’ (provenance, certification, audit) will become a core public good in the digital ecosystem.

< Summary >OpenAI’s research pinpointed the core of AI hallucination issues in ‘evaluation incentives’.Existing benchmarks rewarded guessing, inducing models to make ‘confident lies’.While the GPT-5 family reduced hallucinations by increasing abstention, independent verification still reports a high falsehood rate (around 40%).The most critical economic implication is that digital data contamination can distort GDP estimates, financial signals, and the advertising market.The solution involves a combination of technical (verification-based generation, confidence threshold), operational (human review), and policy-level (source standards, audits) approaches.Businesses, policymakers, and developers must immediately begin implementing evaluations that reward ‘abstention’ and building trust infrastructure.

[Related Articles…]AI Trust Economy, ‘Trust Premium’ Reshaping the Advertising Market — SummaryData Verification Infrastructure is Needed: AI Contamination Response Strategy for GDP and Financial Indicators — Summary

*Source: [ AI Revolution ]

– OpenAI Just Exposed GPT-5 Lies More Than You Think, But Can Be Fixed

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● Google AI Overhaul Business Rewritten, Keyword Ads Obsolete.

Google Update No One Is Ready For — Google’s AI Mode Completely Rewrites Online Business

Key Summary: This article covers how Google’s AI mode changes advertising, the true meaning of keyword targeting shifting to conversational (contextual) targeting, why brand profiles become a targeting strategy, how ad accounts function as AI learning systems, the future of clicks within AI conversations, and a step-by-step execution checklist to prepare for it.Reading this article will provide an immediately actionable 0–90 day checklist, a 3–12 month roadmap, key performance indicators (KPIs), and even crucial secrets rarely discussed in other YouTube videos or news — such as ‘brand profiles, offline signals, and preparing for agentic purchases’.Key SEO Keywords: AI, Google update, online advertising, search engine optimization, digital marketing.

1) Why This Is a Game-Changer in 25 Years

Google has dominated the last 25 years with search results and a click-based billing model.However, AI mode has changed user behavior to solve problems through ‘conversation’.Conversations provide much richer intent signals than single keywords.Google chose to ‘own’ this trend rather than block it, and aims to fully integrate ads into AI mode.Google has already briefed agencies, and a full rollout is expected before Q4 2025.This change transforms the search ecosystem from simple link clicks to ‘agentic transactions’.

2) The End of Keyword Targeting vs. The Birth of Conversational (Contextual) Targeting

Traditional Search: Short keyword searches like “running shoe recommendations” were central.AI Mode Search: Conversations like “I’m preparing for a marathon and have pronation issues. My budget is 300,000 won.” are central.Key Difference: The latter reveals purchase intent all at once, including running level, foot problems, budget, and goals.Result: Ads are designed to respond to the ‘conversational context’ rather than specific keywords.Practical Implication: Product feeds and landing pages must be structured to answer conversational questions.

3) Brand Profile = New Targeting Strategy

Google AI synthesizes a brand profile by integrating brand websites, reviews, social postings, PR, and even ad creatives.If a brand maintains consistent positioning, high reputation, and fresh content, AI will recommend that brand more frequently.Conversely, if the site is outdated and reviews are negative, AI will reduce ad impressions.The key most overlook: It’s not just ad optimization, but the overall ‘data quality’ of the brand that dictates ad performance.

4) Mechanism of Ad Accounts Becoming AI Learning Systems

Ad accounts are now learning databases that provide ‘reward signals’ to Google AI.The quality of potential customers AI finds changes depending on how conversions are defined and rewarded.Example: Simply tracking leads will only increase the number of ‘leads’ but not high-LTV customers.Solution: Set revenue value, repurchases, and customer lifetime value (LTV) as conversion values, and import offline conversions as well.Effect: Over time, like a ‘compounding effect,’ AI will more precisely find valuable customers.

5) Will Clicks Decrease — Potential Changes in Google’s Billing Model

Problem: If AI conversations do not send users to websites, the traditional click-based billing model weakens.Possible Scenarios: Transition to impression-based, conversion-in-AI-based billing, or a hybrid model.What We Know: The intent in conversations is deeper, and the conversion probability is higher.Strategic Response: We must transition from a click-dependent measurement model to value-based (ROAS, LTV) measurement.

6) Must-Do Practical Checklist (Chronological)

Immediately (0–30 days):Full audit of ad accounts, feeds, and tracking (prioritize Feed hygiene).Secure conversion data collection paths with GA4, Conversion API, and server-side tagging.Define key conversions (purchase, sign-up, LTV-based) as values and link them to ads.

Short-term (30–90 days):Add conversational attributes to product feeds (e.g., usage, use case, budget range, problem-solving aspects).Apply schema.org Product, FAQs, HowTo markup to make it easily readable for search engines and AI.Strengthen brand signals through focused review, social, and PR activities.Set up and test Performance Max and Max for Search campaigns.

Mid-term (3–6 months):Execute first-party data strategy (encourage logins, synchronize email and purchase data).Establish automated upload routes for offline conversions (store visits, phone orders).Redesign ad campaigns to be ‘value-based’ and switch to smart bidding.

Long-term (6–12 months+):Prepare product, logistics, and payment processes for AI agents to complete (ensure policies, API, inventory, and price transparency).Strengthen brand positioning (sponsorships, PR, content consistency) to ensure AI’s favorability towards the brand profile.Continuous experimentation: Optimize conversion funnels for traffic driven by ‘conversational queries’.

7) Campaign Structure and Optimization Recommendations

Priority: Start with a Performance Max + Max for Search combination.Invest in ‘broad matching + smart bidding (value-based)’ rather than keyword segmentation.Asset groups should be organized by ‘user conversational scenarios’ (e.g., by budget, by use case, by problem-solving).Adjust feeds and landing pages to include intuitive answers to conversational questions.Regularly run ‘feed hygiene’ routines to maintain metadata accuracy.

8) Key KPIs (Clicks Are No Longer Everything)

Conversion Value per Conversion.LTV : CAC Ratio (Customer Lifetime Value to Customer Acquisition Cost).Direct/Indirect conversions from AI conversations (including assisted conversions if possible).Brand-related metrics: Review scores, new brand search volume, PR mention frequency.Feed quality metrics: Error rate, information freshness, product detail completeness.

9) Risks and Regulatory Considerations

Securing first-party data is essential due to strengthening privacy and data regulations.Increased reliance on Google makes businesses vulnerable to platform risks (fees, policy changes).Potential for regulation: Increased scrutiny of ad billing models, competition law, and consumer protection regulations.Response Strategy: Diversify channels, strengthen proprietary communities and memberships, secure API and contract-based partnerships.

10) Real Game-Changers Rarely Discussed Elsewhere (Special Insights)

Brand ‘speed’ and ‘consistency’ become currency.The ‘frequency and recency’ of reviews serve as AI’s trust indicators.Companies that input offline behaviors (store visits, customer service calls, etc.) as learning data will gain an advantage.In the era of Agentic AI, transparency in pricing, inventory, and cancellation policies will determine sales.Beyond the advertising team, an ‘AI optimization task force’ involving product, CS, logistics, and finance teams must be established.

Practical Example: Running Shoe Brand Immediate Application Scenario

Problem: Brands advertising solely with the keyword ‘running shoes’ face intense competition.Action 1: Add conversational fields to the product feed, such as ‘running level, foot type, cushioning level, recommended budget’.Action 2: Encourage frequent mention of ‘pronation’ in reviews and add corresponding answers to FAQs.Action 3: Set transaction value (6-month repurchase rate after purchase, average revenue) as the conversion value and apply value-based bidding.Effect: When contexts like ‘marathon preparation, pronation, 300,000 won budget’ appear in AI conversations, ads will be prioritized for display, leading to increased conversion rates.

< Summary >Google AI mode replaces keyword-based advertising with conversation-based targeting.A brand’s entire data (web, reviews, social, feeds) becomes a targeting signal.Ad accounts have become systems that train AI, and reward signals (definition of conversion) determine success or failure.Immediate actions include feed hygiene, value-based conversion setup, preparing Performance Max and Max for Search, and strengthening brand trust data.Prepare for potential click reductions by shifting to LTV-centric measurement and establishing an operational system (inventory, payment, policies) for agentic purchases.

[Related Articles…]Redesigning Google Ads: A Practical ChecklistBeyond Performance Marketing to LLM Optimization: The Future of Performance Measurement

*Source: [ Neil Patel ]

– The Google Update No One Is Ready For

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● AI-Driven Cyber Apocalypse New Hacker Economy, Global Markets in Turmoil

Vibe hacking · HexStrike AI · Scattered Lapsus$ · RATs — Cyber and Economic Shocks in the Era of AI Agents and Corporate Response Strategies

This article organizes all major issues covered in the podcast in chronological order.

The contents include — practical cases of vibe hacking and changes in the ‘cost structure’ of hacking, the reality of agent swarms created by open tools like HexStrike AI, the dynamics of AI agent attacks versus human attacks, the socio-economic repercussions of Scattered Lapsus$’s unusual extortion method (demanding employee dismissals), the impact of increased RATs (Remote Access Trojans) on corporate assets and the data economy, and critical points and practical/policy recommendations often not discussed in other YouTube videos or news outlets.

Key aspects often not covered by other media include the “redesign of hacker business models” and the “vulnerability of the AI supply chain and API economy.”

01. 00:00–1:40: Overall Context and Why This Issue Is Important

Why discuss this now?

Because AI and agent technologies are lowering the barrier to cyberattacks, leading to a rapid increase in attack frequency, speed, and economic impact.

Due to the interconnectedness of the global economy and digital transformation, cyber incidents go beyond mere security issues to influence inflation, interest rates, and investor sentiment.

02. 1:40–9:28 — Vibe hacking: Concept and Real-world Cases

Definition: Vibe hacking is a trend where LLMs (Large Language Models) are used not only for simple code generation but also for tactical and strategic decision-making in carrying out attacks.

Case Summary: A threat actor used tools like Claude Code to ask the model what data to extract, how much ransom to demand, and then executed the actual attack.

Key Implication: Humans are reduced to ‘prompt managers,’ while AI takes charge of attack design and the generation of polymorphic (ever-changing) malware.

Practical Implications: Attack automation and decision-making automation significantly increase the complexity of detection and response.

03. 9:28–14:42 — HexStrike AI: The Mechanism of Defensive Tools Being Abused for Attacks

HexStrike AI is an agent orchestration framework aimed at legitimate red teaming and penetration testing.

The problem arises ‘when it falls into the wrong hands.’

Attackers can use HexStrike to operate hundreds or thousands of agents in parallel and asynchronously, dramatically accelerating vulnerability discovery and exploit development.

Consequently, the ‘public release and proliferation of tools’ drastically increases the workload and costs for defenders.

04. 14:42–18:16 — AI Agent Attacks vs. Human Attacks: Differences in Choice and Response

AI Advantages: Speed, 24/7 operation, data-driven decision-making, and polymorphic (variability) capabilities to evade detection.

Human Attacker Advantages: Creative circumvention, social engineering, unpredictability, and utilization of non-standard procedures.

Practical Judgment: In the short term, both will coexist, and defenders must simultaneously prepare for ‘AI vs. AI’ and maintain defenses against traditional human-centric attacks.

Operational Tip: There’s a possibility of attempting counterattacks by inducing agent malfunctions (hallucinations), but this carries risks and requires legal and ethical review.

05. 18:16–26:03 — Scattered Lapsus$ Hunters’ ‘Demand for Employee Dismissal’ Method and Economic Repercussions

Incident Summary: A Scattered Lapsus$ affiliate claimed to have exfiltrated Google’s internal data and demanded the dismissal of two specific employees.

New Aspect: This is a type of extortion demanding ‘action (employee dismissal)’ rather than money, which creates new challenges for negotiation structures and legal responses.

Why It’s Important: If a company accepts the demand, it sets a ‘precedent,’ leading to additional social costs and uncertainties (stock price and trustworthiness decline).

Policy Implications: Companies should pre-establish a policy of refusing ‘non-monetary demands’ and immediately activate communication and legal strategies during a crisis.

06. 26:03–end — Increase in RATs (Remote Access Trojans): Technical and Economic Impact

Trend: According to a Recorded Future report, RAT usage is increasing in H1 2025.

Definition Difference: RATs have the capability to go beyond simple information exfiltration, taking control of systems, cameras, microphones, and even stealing ‘behavior and identity.’

Economic Impact: If personal or corporate data, images, or biometric information is leaked, it leads to loss of trust and an increase in litigation, regulatory costs, and insurance premiums.

Practical Response: Essential steps include basic defenses (patching, EDR, behavior-based detection), adoption of passwordless and secret management (vault) solutions, and transition to a Zero Trust architecture.

07. Key Points Often Not Discussed by Other Media (Most Important Content)

1) Redesign of Hacker Revenue Models — AI lowers the ‘cost of hacking,’ encouraging the influx of new attackers.

2) Agent Economy — If a ‘rental-type’ market for malicious agents emerges, the transaction and contract structures on the dark web will become as complex as financial markets.

3) AI Supply Chain and API Vulnerabilities — Inadequate management of vendors, APIs, and tokens providing models enables the ‘mass production’ of attacks.

4) Impact on Insurance and Financial Systems — Rising loss ratios for cyber insurance will have a cascading effect on financial institutions’ asset values, interest rates, and premiums.

5) Regulatory and International Policy Gaps — Differences in national regulations provide ‘safe havens’ for attackers.

08. Practical Recommendations Organized Chronologically (for Companies, CISOs, and Policymakers)

Enterprise (Operations Team) — Priorities: Patching, EDR, centralized logging, network segmentation.

Enterprise (Product Team) — Apply an ‘assume breach’ mindset during product design and make secret management, passwordless authentication, and least privilege design the default.

CISO — Introduce defensive agents leveraging AI and establish AI security governance (prompt auditing, model access control).

Management/Board of Directors — Translate cyber risks into financial risks, prepare management plans for various loss scenarios, and allocate insurance/reserves (risk leverage).

Policymakers — Urgently pursue AI/model supply chain regulations, API security standards, and the establishment of international cooperation frameworks.

09. Impact Analysis from a Global Economic Perspective

An increase in cyber incidents leads to a decline in corporate trust, triggering reduced investment and increased costs.

In the short term, this can exert upward pressure on inflation through rising operational costs and supply chain delays.

In the medium to long term, there is a high probability that the cost of capital (interest rate sensitivity) will increase due to a rise in the risk premium for digital assets.

Therefore, central banks and financial authorities must monitor cyber risks from the perspective of financial stability.

10. Checklist for Immediate Corporate Application (in priority order)

1) Update emergency response playbooks (including non-monetary extortion) and conduct simulation drills.

2) Implement passwordless authentication and enforce secret (vault) policies.

3) Implement EDR/network behavior-based detection and strengthen log retention policies.

4) Establish a ‘prompt auditing’ system with LLM/API access rights, token management, and prompt logging.

5) Re-evaluate cyber insurance coverage and exclusions, and conduct a cost-benefit analysis.

11. Summary Decision-Making Guide for Investors and Policymakers

Investors: Apply a risk premium to the valuation of sectors with high cyber risk (e.g., FinTech, healthcare).

Policymakers: The swift establishment of AI/cybersecurity standards, international cooperation, and breach notification obligations is necessary.

Market Makers: Reduce systemic risk by ensuring transparency of cyber risk data and strengthening the soundness of the insurance market.

12. Final Conclusion: What to Do First

First, immediately review ‘fundamentals’ (patching, logs, EDR, passwordless authentication).

Second, establish governance and a prompt auditing system to make AI part of defense, and manage vendor risks.

Third, management should convert cyber risks into financial risks, securing loss scenarios and budgets.

< Summary >

Vibe hacking significantly lowers the cost and barrier to hacking by leveraging LLMs for attack design and decision-making.

Frameworks like HexStrike, when legitimate tools are misused, can generate swarms of agents, drastically increasing defensive difficulty.

Scattered Lapsus$’s ‘demand for employee dismissal’ illustrates the risk of non-monetary extortion, requiring companies to adopt strict refusal and response strategies to avoid setting precedents.

The increase in RATs has a direct economic impact on the asset value and data trustworthiness of individuals and businesses, with cascading effects on insurance premiums, interest rates, and investor sentiment.

The most crucial points are the ‘change in hacker business models’ and the vulnerabilities of the AI supply chain and APIs, which are not merely technical issues but financial and policy concerns.

Practically, strengthening basic security hygiene, implementing passwordless authentication, Zero Trust, prompt auditing, and financial preparedness by management are priorities.

[Related Articles…]

AI and the Global Economy: The Correlation Between Inflation and Interest Rates in 2025 — Summary

Cybersecurity Trends: Responding to RAT Threats in the Era of Digital Transformation — Summary

*Source: [ IBM Technology ]

– Vibe hacking, HexStrike AI and the latest scheme from Scattered Lapsus$ Hunters

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● Quantum’s Cold War Components, Cooling Reshape Finance, Global Power

The Future of Commercialized Quantum Computers? 10 Key Insights — Realistic Timelines for Each QPU, Practical Applications in Finance and Logistics, and South Korea’s Essential ‘Parts & Packaging’ Strategy Revealed

First, here are the key topics covered in this article.

• Pros and cons of the four main types of QPUs (Quantum Processing Units) and commercialization scenarios.
• Practical bottlenecks not often covered in other news: the hidden hurdles to commercialization posed by wiring, cooling, packaging, laser, and optical component issues.
• Practical changes in finance and logistics where optimization considering the ‘time dimension’ becomes possible.
• Quantum-classic hybrid practical workflows and 7 immediate actions companies should prepare.
• South Korea’s strategy for core materials, parts, and equipment (SooBuJang) and regulatory/workforce roadmap, achievable through a 1,000-qubit goal.
  You’ll find immediately actionable strategies and key risks/opportunities not often discussed in other YouTube videos/news, all at a glance.

1) Present (Status Quo) — A Realistic Overview of 4 QPU Types

Superconducting QPU — Characteristics and Realistic Constraints.

• Pros: Easy to scale up chip-based systems by leveraging existing semiconductor manufacturing experience.
• Cons: Requires cooling to near absolute zero (several mK), and limitations in cryocoolers and wiring are bottlenecks for scaling.
• Commercialization Stage: Big tech companies like IBM and Google are leading with a cloud-based approach.
  Trapped Ion (QPU) — Characteristics and Application Potential.
• Pros: High-fidelity qubits (naturally provided), high gate accuracy (around 99.9%).
• Cons: Limited scalability of electric field trap designs, requiring high costs for laser control.
• Leading Companies: Quantinuum, IonQ affiliates.
  Neutral Atom (QPU) — Advantageous for Large-Scale Qubits.
• Pros: Can control tens to thousands of atoms with optical tweezers, flexible arrangement allows for various entanglement topologies.
• Cons: Requires delicate optical control (lasers, lenses, mirrors, etc.) and operational “finesse.”
  Photon-based QPU — Potential for Room-Temperature Operation.
• Pros: Operates at room temperature, no cooling required, enables large-scale parallelization on photonic integrated chips.
• Cons: Difficult to control photon-photon interactions, limiting types of operations and error rate control.
  Molecular-based Approach (Research-focused) — Advantage of Multiple Control “Handles.”
• Features: Precise control possible through multiple degrees of freedom of molecules, advantageous for specific simulation and sensing applications.
• Cons: Increased tuning complexity due to numerous control variables.

2) Short-Term (1-3 Years) — Cloud and Hybrid Proof-of-Concept Stage

Situation Summary: Currently, each QPU type offers limited services in the form of cloud APIs.
Key Changes: Companies are beginning PoCs (Proof of Concept) utilizing hybrid workflows (classic CPU/GPU + quantum QPU).
Key Application Areas (Short-term Priority):

• Finance: Portfolio optimization, risk scenario calculation, accelerated testing of option pricing.
• Logistics: Initial PoC for space-based route optimization.
• Materials/Chemistry: Research acceleration experiments as a pre-stage (approximate model) for molecular simulation.
  What Companies Should Prepare Immediately:
  1) Standardize data formats and APIs for quantum access.
  2) Secure personnel for hybrid algorithms (quantum pre- and post-processing).
  3) Begin exploring quantum-safe cryptography.

3) Mid-Term (3-7 Years) — Error Correction, Scaling, and Widespread Real-World Scenarios

Technological Evolution: Error correction and logical qubit expansion increase practical problem-solving capabilities.
Expansion of Real-World Use Cases:

• Finance: Tangible benefits in optimization and risk management (portfolio rebalancing, risk hedging calculations) rather than high-frequency trading.
• Logistics: ‘Time + space’ optimization becomes possible, enabling real-time optimization of inventory and delivery.
• Energy: Cost reduction through time-series optimization of distributed generation (solar, ESS) allocation.
  Economic and Institutional Issues:
• Centralization Risk: Concern over market concentration as large-scale quantum cloud-owning companies (big tech, major financial institutions) gain computational advantages.
• Need for Regulation: A regulatory sandbox is needed for the impact of quantum-computation-powered strategies on financial market stability.

4) Long-Term (7-15 Years) — Commercialization, Industrial Restructuring, and Risks

Imaginable Changes: Quantum computers will act as backend enablers, accelerating optimization across industries and the development of new materials.
Major Economic Impacts: Productivity improvements, changes in energy and logistics cost structures, industrial restructuring through new materials and battery innovation.
Risk Factors:

• Potential for Cryptographic Breakage: At the point of quantum advantage (quantum computers decrypting classical cryptography), a massive shift in electronic finance and communication security will be necessary.
• Technology Concentration: If quantum computing capabilities become concentrated in developed countries and large corporations, global economic imbalances could deepen.

5) The ‘Most Important’ Infrastructure and Industrial Points Rarely Discussed Elsewhere

1) Wiring, cooling, and packaging issues are more about ‘physical limitations’ than ‘cost.’

• Superconducting methods face fundamental scaling limitations due to cooling capacity and noise issues as the number of wires entering the cryocooler increases.
• Beyond expensive equipment, this issue makes the design of ‘data center-type quantum rooms,’ power/thermal management, and the packaging industry critical core components (SooBuJang).
  2) Optical, laser, and nanophotonic components are commercialization bottlenecks.
• Neutral atom, trapped ion, and photon-based methods rely on precision lasers, lenses, and integrated optical components as their core.
• South Korea should leverage its strengths and focus first on laser and optical SooBuJang to gain competitiveness.
  3) ‘Time-dimension optimization’ in logistics represents a qualitative difference from existing optimization methods.
• Simple route optimization expands to time-series optimization, integrating production/demand forecasting, travel routes, storage, and time windows.
• This change leads to the redesign of existing data engineering methods and ERP systems.
  4) Market power concentration and regulatory issues in finance are as important as the technology itself.
• If financial institutions gain an absolute advantage in ultra-short-term and time-series decision-making through quantum superiority, issues of market fairness and liquidity could arise.

6) Strategic Recommended Actions from a South Korean (nextgeninsight.net/) Perspective

Short-Term (1 year) Recommendations:

• Establish a national ‘quantum workforce development program’ and joint industry-academia-research educational courses.
• Initiate the establishment of a quantum-safe cryptography standard and implementation roadmap.
  Mid-Term (1-4 years) Recommendations:
• Focus investment on core materials, parts, and equipment (SooBuJang) such as lasers, optics, cryogenics, and precision wiring.
• Establish industry-specific (finance, logistics, energy) cube-type proof-of-concept testbeds and operate regulatory sandboxes.
  Long-Term (4-10 years) Recommendations:
• Secure SooBuJang competitiveness and production pipelines through a ‘domestic first complete unit’ project (e.g., aiming for 1,000 qubits).
• Foster a cloud-based ‘Quantum SaaS’ ecosystem: support small and medium-sized enterprises to utilize quantum computing via APIs.
  Policy Recommendations:
• Provide R&D and tax incentives for fostering SooBuJang.
• Allocate quantum PoC budgets for public data and projects to stimulate industrial demand.

7) Practical Checklist for Companies and Developers to Act On Now

1) Identify internal data and algorithms where ‘quantum advantage’ is expected (e.g., optimization, approximation, similarity search).
2) Obtain quantum cloud (API) test accounts and begin developing hybrid protocols.
3) Develop a quantum-safe cryptography transition plan with legal and security teams.
4) Partnerships: Establish collaboration agreements with laser/optics/SooBuJang companies and university research labs.
5) Talent: Recruit and train not only quantum algorithm specialists but also experts in optics, materials, and system integration.

8) The Convergence of AI Trends and Quantum Computing: Quantum AI Outlook

Main Idea: Quantum has the potential to solve AI bottlenecks in ‘data search, similarity comparison, and specific optimizations.’

• Example: Quantum acceleration in large-scale embedding search (Nearest Neighbor) can dramatically shorten similarity search times.
• Improved decision-making with small data: Quantum-based sampling and inference offer better generalization possibilities in data-scarce domains.
  Realistic Path: Quantum-classical hybrid models will remain dominant for the foreseeable future.
• Key: Only ‘core calculations’ are offloaded to quantum, while pre-processing, post-processing, and large-scale data management are handled by existing infrastructure.

9) Cryptography and Security: Timeline for Preparation

• There’s no immediate need to change all cryptography right now.
• However, the financial, public, and telecommunications sectors should begin designing a transition to quantum-safe cryptography (quantum-resistant) within 3-7 years.
• Recommendation: Establish a gradual migration plan for critical private and public systems through ‘dualization (legacy + PQC).’

10) Summary: Investment Priorities (South Korea Standard)

1) SooBuJang (Core Materials, Parts, and Equipment): Lasers, optics, cooling, precision wiring, packaging.
2) Workforce: Fostering interdisciplinary talent (physics + engineering + computer science).
3) Industrial PoC: Time-series optimization pilots in finance, logistics, and energy.
4) Regulation and Standards: Quantum-safe cryptography and regulatory sandboxes for finance.
5) Cloud and Middleware: Nurturing a software ecosystem to connect quantum computations as a service.

< Summary >

• QPU types like superconducting, trapped ion, neutral atom, and photon-based clearly have distinct pros and cons.
• The commercialization bottleneck is not merely the number of qubits, but rather issues with SooBuJang (components) like cooling, wiring, lasers, optics, and packaging.
• The short term will see cloud-based hybrid PoCs, the mid-term will bring error correction and real-world applications, and the long term will involve industry-wide optimization and new material innovation.
• Finance, logistics, and energy are the largest early demand sectors, with logistics, in particular, expected to undergo qualitative changes through ‘time + space’ optimization.
• South Korea should differentiate itself with a strategy focused on SooBuJang, packaging, and optics rather than competing on finished products, requiring concentrated investment from industry, academia, research institutions, and the government.

[Related Articles…]
The Future of AI and Finance: The Evolution of Algorithmic Trading — What the Financial Industry Should Prepare for in the AI and Quantum Era
Global Supply Chain Restructuring and Logistics Innovation Strategy — Logistics Optimization, Next-Generation Operations with the Time Axis

*Source: [ 삼성SDS ]

– 양자컴퓨터가 상용화 된 미래? 💸 돈 복사 들어 갑니다!! 양자컴퓨팅 전망 ㅣ 김덕진의 ㅅㄷㅅ 찐테크

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● GS Caltex, Samsung SDS GDC New Global IT Standard, AI-Ready.

Even reading only the first sentence, the core insights of this article are visible.This article covers the background of GS Caltex’s adoption of Samsung SDS GDC and the chronological implementation process.It also includes the ‘real’ cost structure changes not apparent on site, and practical solutions for security and quality measurement.Communication secrets for Vietnam DDC collaboration (glossary standardization) and strategies for talent and governance transition.Furthermore, it contains a checklist immediately applicable from the perspective of DX, Cloud, and Generative AI, along with risk mitigation strategies.

GS Caltex’s Choice for a New Standard in Global IT Operations: Samsung SDS GDC Application Case and Significance

1) Background — Why GDC?

GS Caltex has prioritized production and operational efficiency and stability throughout its 58-year history.Recently, there has been a strong demand to redesign work processes based on systems and data through Digital Transformation (DX).As DX expanded, the number of IT systems to operate surged, leading to issues of increased operating costs and management complexity.Consequently, there was a need to adopt a GDC (Global Delivery Center) equipped with cloud-based Global IT Operation standards and automation/quality management tools.

2) Challenges — Real-world Problems on Site (Chronological Explanation)

Early Stage: As the number of systems grew due to increased DX projects, limitations in operational expertise and management capabilities were exposed.Mid Stage: Rising labor costs (e.g., 50% increase in labor costs after ERP implementation) and fixed budget constraints necessitated securing cost efficiency.Simultaneously: Concerns about security and quality (assurance of quality and security), and communication issues in global collaboration (Vietnam DDC) also existed.Conclusion: What was needed was not simple outsourcing, but a partner who could transfer ‘tools, frameworks, and operating methods’.

3) Decision-Making & Implementation Stages

Evaluation Stage: Verification focused on the quality and security measurement tools, certifications, and actual operational cases provided by Samsung SDS GDC.Priority Setting: On-premise infrastructure migration was performed first.Implementation Stage: Common applications such as HR, legal, and procurement were migrated first to minimize internal impact and ensure operational stability.Transfer and Collaboration: As GDC’s operational framework and tools were systematically transferred within GS Caltex, operational quality gradually improved.

4) Detailed Strategies by Key Implementation Activities

Communication Standardization: The company’s unique glossary was reorganized to enhance communication accuracy with the Vietnam DDC.Quality and Security Measurement: By adopting automated quality and security measurement tools already possessed by GDC, monitoring and metric tracking became possible.Priority Migration Strategy: High-impact on-premise infrastructure was migrated first to reduce risks.Cost Structure Optimization: Development and operating costs were separated, allowing DDC to reduce development costs while managing more systems without increasing operating costs.Framework Transfer: Samsung SDS’s tools and framework were delivered along with actual operational know-how, fostering even cultural changes.

5) Measurable Achievements and Impact

Direct Achievements: Achieved reduction in development costs and stabilization of operating costs.Alleviation of Labor Cost Pressure: Even with a 50% increase in labor costs, the overall budget was maintained, allowing for the formulation of a plan for ERP reintroduction.Increased Quality and Security Reliability: Enhanced visibility due to certifications and measurement tools accelerated internal decision-making.Operational Flexibility: Experienced improved incident response speed and stability through agile development and automated operations.

6) Key Insights Not Well Covered in Other News/YouTube (Most Important Content)

Glossary and communication standardization were, in fact, the ‘greatest value’.More important than apparent cost savings was ‘knowledge transfer’, where the handover of operational frameworks, proven procedures, and tools builds long-term competitiveness.GDC adoption is not merely a transfer of personnel but a synchronization of the company’s operational logic (operational SOPs, quality metrics, security inspection processes) with external entities.The data availability gained from this process becomes fuel for subsequent AI and analytics projects (enabling Generative AI-based automation and advanced analytics).Therefore, GDC is not only about cost savings but also an ‘infrastructure investment’ for securing future AI and data-driven competitiveness.

7) Practical Know-how for Global Collaboration — Vietnam DDC Case

Even with personnel capable of practical communication in Korean, misunderstandings arise without glossary and task standardization.Solution: A combination of a core glossary + business scenario (use case) templates + interpretation support strategy was implemented.Result: Collaboration speed with local personnel improved, and quality tracking became possible with unified metrics.

8) Synergy Points with Generative AI (Gen AI)

Operational Automation: Operational logs and quality metrics accumulated in GDC were immediately usable as training data for Generative AI agents.Case Application: AI agents were applied to repetitive ticket processing, log analysis, and initial diagnosis of fault causes, significantly reducing manual work.Caution: Ensuring data governance and labeling quality is essential before AI application.Opportunity: AI amplifies DX effects not only through task automation but also through decision support and predictive maintenance.

9) Risks, Limitations, and Mitigation Strategies

Risks: Vendor dependency (potential lock-in), data sovereignty and regulatory issues, failure of cultural transfer.Mitigation Strategy: Clearly define SLA, metrics, and knowledge transfer clauses during the contract phase.Mitigation Strategy: Require regular third-party security and quality audits and certifications.Mitigation Strategy: Combine internal capability building (onboarding/training) with documentation (operation manuals/glossaries).

10) Practical Application Checklist (Immediate Actions)

1) Priority: Establish a migration plan in the order of on-premise infrastructure -> common services (HR/Procurement) -> business critical applications.2) Glossary and Task Standardization: Create a core glossary and business scenario templates.3) Quality and Security KPIs: Implement automated measurement tools and establish initial baselines.4) Contract Terms: Include clauses for knowledge transfer, data accessibility, and decentralization (anti-lock-in).5) AI Readiness: Design log and event data storage policies and a labeling pipeline.6) Governance: Establish plans for regular reports, SLA monitoring, and third-party audits.

11) Execution Roadmap (Recommended Schedule by Phase)

0-3 Months: Diagnosis and prioritization, glossary creation, contract signing.3-9 Months: Infrastructure and common services migration, quality measurement tool implementation, initial SLA testing.9-18 Months: Core business application migration, AI pilot (operational automation) implementation.18-36 Months: Enterprise-wide standardization, continuous improvement, and global standardization expansion.

< Summary >GS Caltex resolved the increased IT complexity driven by DX initiatives through the adoption of Samsung SDS GDC.The core value lies in securing long-term competitiveness through ‘operational framework and knowledge transfer’ rather than just cost savings.Glossary standardization and the introduction of automated quality and security metrics significantly enhanced collaboration efficiency.Risks were reduced through a strategy of prioritizing on-premise migration and common service migration.By establishing data governance, DX effects can be expanded through operational automation and predictive maintenance using Generative AI.

[Related Articles…]GDC’s Transformation of Global IT Operating Strategy: Real-world Cloud Transition CaseEnterprise DX Practical Guide Driven by Generative AI: Operational Automation and Data Governance

*Source: [ 삼성SDS ]

– [고객사례] GS칼텍스가 선택한 글로벌 IT 운영의 새로운 표준 🌏 삼성SDS GDC

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