#enterprise data warehouse

[thedatachannel]

Data Modelling Master Class-Series | Introduction -Topic 1

https://youtu.be/L1x_BM9wWdQ

#theDataChannel @thedatachannel @datamodelling

[hanasatoblogs]

Big Data vs. EDW: Can Modern Analytics Replace Traditional Data Warehousing?

As organizations increasingly rely on data to drive business decisions, a common question arises: Can Big Data replace an EDW (Enterprise Data Warehouse)? While both play crucial roles in managing data, their purposes, architectures, and strengths differ. Understanding these differences can help businesses decide whether Big Data technologies can entirely replace an EDW or if a hybrid approach is more suitable.

What Does EDW Stand for in Data?

An EDW or Enterprise Data Warehouse is a centralized repository where organizations store structured data from various sources. It supports reporting, analysis, and decision-making by providing a consistent and unified view of an organization’s data.

Big Data vs. EDW: Key Differences

One of the primary differences between Big Data and enterprise data warehousing lies in their architecture and the types of data they handle:

Data Type: EDWs typically manage structured data—information stored in a defined schema, such as relational databases. In contrast, Big Data platforms handle both structured and unstructured data (like text, images, and social media data), offering more flexibility.

Scalability: EDWs are traditionally more rigid and harder to scale compared to Big Data technologies like Hadoop and Spark, which can handle massive volumes of data across distributed systems.

Speed and Performance: EDWs are optimized for complex queries but may struggle with the vast amounts of data Big Data systems can process quickly. Big Data’s parallel processing capabilities make it ideal for analyzing large, diverse data sets in real time.

Big Data Warehouse Architecture

The Big Data warehouse architecture uses a distributed framework, allowing for the ingestion, storage, and processing of vast amounts of data. It typically consists of:

Data Ingestion Layer: Collects and streams data from various sources, structured or unstructured.

Storage Layer: Data is stored in distributed systems, such as Hadoop Distributed File System (HDFS) or cloud storage, allowing scalability and fault tolerance.

Processing Layer: Tools like Apache Hive and Apache Spark process and analyze data in parallel across multiple nodes, making it highly efficient for large data sets.

Visualization and Reporting: Once processed, data is visualized using BI tools like Tableau, enabling real-time insights.

This architecture enables businesses to harness diverse data streams for analytics, making Big Data an attractive alternative to traditional EDW systems for specific use cases.

Can Big Data Replace an EDW?

In many ways, Big Data can complement or augment an EDW, but it may not entirely replace it for all organizations. EDWs excel in environments where structured data consistency is crucial, such as financial reporting or regulatory compliance. Big Data, on the other hand, shines in scenarios where the variety and volume of data are critical, such as customer sentiment analysis or IoT data processing.

Some organizations adopt a hybrid model, where an EDW handles structured data for critical reporting, while a Big Data platform processes unstructured and semi-structured data for advanced analytics. For example, Netflix uses both—an EDW for business reporting and a Big Data platform for recommendation engines and content analysis.

Data-Driven Decision Making with Hybrid Models

A hybrid approach allows organizations to balance the strengths of both systems. For instance, Coca-Cola leverages Big Data to analyze consumer preferences, while its EDW handles operational reporting. This blend ensures that the company can respond quickly to market trends while maintaining a consistent view of critical business metrics.

Most Popular Questions and Answers

Questions: Can Big Data and EDW coexist?

Answers: Yes, many organizations adopt a hybrid model where EDW manages structured data for reporting, and Big Data platforms handle unstructured data for analytics.

Questions: What are the benefits of using Big Data over EDW?

Answers: Big Data platforms offer better scalability, flexibility in handling various data types, and faster processing for large volumes of information.

Questions: Is EDW still relevant in modern data architecture?

Answers: Yes, EDWs are still essential for organizations that need consistent, reliable reporting on structured data. However, many companies also integrate Big Data for advanced analytics.

Questions: Which industries benefit most from Big Data platforms?

Answers: Industries like retail, healthcare, and entertainment benefit from Big Data’s ability to process large volumes of unstructured data, providing insights that drive customer engagement and innovation.

Questions: Can Big Data handle structured data?

Answers: Yes, Big Data platforms can process structured data, but their true strength lies in handling unstructured and semi-structured data alongside structured data.

Conclusion

While Big Data offers impressive capabilities in handling massive, diverse data sets, it cannot completely replace the functionality of an Enterprise Data Warehouse for all organizations. Instead, companies should evaluate their specific needs and consider hybrid architectures that leverage the strengths of both systems. With the right strategy, businesses can harness both EDWs and Big Data to make smarter, faster decisions and stay ahead in the digital age.

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[johnsongray22]

Benefits of Using Enterprise Data Warehouses in the Finance Industry

Looking to improve financial processes with an Enterprise Data Warehouse? Read the blog to learn the advantages of an Enterprise Data Warehouse and how it can help improve your finance industry.

[ryanwilliamsonstuff]

Complete Guide to Building a Data Warehouse for Enterprise

Know what is an enterprise data warehouse (EDW), its architecture, features, types, integrations, benefits, tools, and more.

[lisakeller22]

Advantages of Enterprise Data Warehouse for Your Data-driven Organization

This article showcases what is an enterprise data warehouse (EDW), its components, and the top benefits of EDW for businesses.

[raurquiz]

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[sanikavijaymane]

Enterprise Data Warehouse Market to Hit $8.15B by 2032

Meticulous Research®—a leading global market research company, published a research report titled, ‘Enterprise Data Warehouse Market by Offering (Solution, Services), Organization Size, Deployment Mode, End-use Industry (IT & Telecommunication, Healthcare, Retail & E-commerce, BFSI, Manufacturing), and Geography - Global Forecast to 2032.’

Enterprise Data Warehouse Market Booming with Growing Demand for Analytics

The enterprise data warehouse (EDW) market is on a strong growth trajectory, projected to reach $8.15 billion by 2032 at a CAGR of 14.4%, says, Meticulous Research®. This surge is driven by:

Importance of Business Intelligence: Businesses increasingly rely on data analytics and business intelligence for informed decision-making, making EDW a crucial tool.

Cloud Adoption Boosts Accessibility: The growing deployment of cloud-based EDWs offers scalability, cost-effectiveness, and easier data access, attracting more users.

Focus on Data Security and Compliance: EDW solutions provide advanced security features and compliance certifications, addressing data privacy concerns.

EDW Market Challenges and Opportunities

Despite its potential, the market faces hurdles:

Data Management Complexity: Managing and optimizing data structures within EDWs requires ongoing effort.

Key Trends Shaping the Future

Machine Learning and AI Integration: The integration of AI and Machine Learning promises to enhance data analysis capabilities and automate tasks within EDWs.

Virtual Data Warehousing: Virtual data warehousing facilitates easier data access across various sources, improving data-driven decision making.

Market Segmentation Highlights

The report explores various segments within the EDW market:

Offerings: Solutions (data warehousing & integration, data governance, etc.) dominate due to the need for data management, analytics, and security tools.

Organization Size: Large enterprises hold the current lead, but the Small & Medium-sized Enterprise (SME) segment is expected to see the fastest growth due to increasing EDW adoption for improved data insights.

Deployment Mode: Cloud-based deployments are gaining traction due to affordability, scalability, and automatic updates. However, on-premise solutions remain preferred by some enterprises for security and control.

End-Use Industry: IT & Telecommunications hold the largest share, but the healthcare sector is seeing rapid growth due to the need for data-driven patient care and disease management.

Geography: North America leads the market, but Asia-Pacific is expected to witness the fastest growth due to technological advancements and rising data awareness.

By understanding these trends and segmentation, businesses can leverage the power of EDW solutions to gain valuable insights and make data-driven decisions for improved performance.

Key Players:

The key players operating in the enterprise data warehouse market include Accur8 Software (U.S.), Virtusa Corporation (U.S.), International Business Machines Corporation (U.S.), Microsoft Corporation (U.S.), Oracle Corporation (U.S.), SAP SE (Germany), Snowflake Inc. (U.S.), HCL Technologies Ltd. (India), Amitech Solutions, Inc. (U.S.), Fusion Consulting AG (Switzerland), Micro Focus International Limited (U.K.) (A Subsidiary of OpenText Corporation), Health Catalyst, Inc. (U.S.), AtScale (U.S.), CitiusTech Inc. (U.S.), and Cloudera, Inc. (U.S.).

Download Sample Report Here @

 

ContactUs:MeticulousResearch® Email- sales@meticulousresearch.com ContactSales-+1-646-781-8004 Connect with us on LinkedIn- https://www.linkedin.com/company/meticulous-research

[polestarnalytics]

Master Enterprise Data Warehouse implementation in 2025 with this expert-backed guide by Polestar Analytics. Dive deep into strategic approaches, best practices, and real-world tips to successfully implement and scale enterprise-level data warehousing solutions. Learn how to build a robust architecture, ensure data integrity, and align your warehouse with business goals. Whether you're modernizing legacy systems or launching a new initiative, this guide simplifies complex concepts and empowers data leaders to drive smarter decision-making. Stay ahead of the curve with practical insights into tools, frameworks, and processes that define successful enterprise data warehousing today.

[marketreaders]

Enterprise Data Warehouse (EDW) Market Report 2025-2033: Trends, Opportunities, and Forecast

Enterprise Data Warehouse (EDW) Market Size

The global enterprise data warehouse (EDW) market size was valued at USD 3.43 billion in 2024 and is estimated to reach USD 22.36 billion by 2033, growing at a CAGR of 19.29% during the forecast period (2025–2033).

Enterprise Data Warehouse (EDW) Market Overview:

The Enterprise Data Warehouse (EDW) Market The report provides projections and trend analysis for the years 2024–2033 and offers comprehensive insights into a market that spans several industries. By fusing a wealth of quantitative data with professional judgment, the study explores important topics such product innovation, adoption rates, price strategies, and regional market penetration. Macroeconomic variables like GDP growth and socioeconomic indices are also taken into account in order to put market swings in perspective. An Enterprise Data Warehouse is a centralized repository designed to store, manage, and analyze vast amounts of structured and unstructured data from various sources across an organization. It serves as a critical component for business intelligence (BI), enabling organizations to consolidate data from different departments or systems into a single, cohesive view. This allows for comprehensive data analysis, reporting, and decision-making. The main market participants, the industries that employ the products or services, and shifting consumer tastes are all crucial subjects of conversation. The competitive environments, regulatory effects, and technical advancements that affect the market are all carefully examined in this study. The well-structured Enterprise Data Warehouse (EDW) Market Report provides stakeholders from a variety of political, cultural, and sectors with useful commercial information.

Get Sample Research Report: https://marketstrides.com/request-sample/enterprise-data-warehouse-edw-market

Enterprise Data Warehouse (EDW) Market Growth And Trends

Numerous Enterprise Data Warehouse (EDW) Market breakthroughs are driving a significant shift in the industry, altering its course for the future. Following these important changes is essential because they have the potential to reshape operations and plans. Digital Transformation: Data-driven solutions enhance customer contact and streamline processes as digital technologies develop. Customer Preferences: Businesses are offering customized items as a result of the growing emphasis on convenience and personalization. Regulatory Changes: Companies must quickly adjust in order to stay competitive as compliance standards and rules become more stringent.

Who Are the Key Players in Enterprise Data Warehouse (EDW) Market , and How Do They Influence the Market?

Amazon Web Services (AWS)

Microsoft Azure

Google Cloud

Snowflake

Oracle

IBM

SAP

Teradata

Cloudera

Hewlett Packard Enterprise (HPE)

Alibaba Cloud

Dell Technologies

Hitachi Vantara

Informatica

Huawei

With an emphasis on the top three to five companies, this section offers a SWOT analysis of the major players in the Enterprise Data Warehouse (EDW) Market market. It highlights their advantages, disadvantages, possibilities, and dangers while examining their main strategies, present priorities, competitive obstacles, and prospective market expansion areas. Additionally, the client's preferences can be accommodated by customizing the company list. We evaluate the top five companies and examine recent events including partnerships, mergers, acquisitions, and product launches in the section on the competitive climate. Using the Ace matrix criteria, their Enterprise Data Warehouse (EDW) Market market share, growth potential, contributions to total market growth, and geographic presence and market relevance are also analyzed.

Browse Details of Enterprise Data Warehouse (EDW) Market with TOC: https://marketstrides.com/report/enterprise-data-warehouse-edw-market

Enterprise Data Warehouse (EDW) Market : Segmentation

By Deployment

Web Based

Server

Hybrid

By Product Type

Information Processing

Data Mining

Analytical Processing

Others

By Data Type

Billings

Documents

Records

Financials

Others

What Makes Our Research Methodology Reliable and Effective?

Data Accuracy & Authenticity – We use verified sources and advanced data validation techniques to ensure accurate and trustworthy insights.

Combination of Primary & Secondary Research – We gather first-hand data through surveys, interviews, and observations while also leveraging existing market reports for a holistic approach.

Industry-Specific Expertise – Our team consists of professionals with deep domain knowledge, ensuring relevant and actionable research outcomes.

Advanced Analytical Tools – We utilize AI-driven analytics, statistical models, and business intelligence tools to derive meaningful insights.

Comprehensive Market Coverage – We study key market players, consumer behavior, trends, and competitive landscapes to provide a 360-degree analysis.

Custom-Tailored Approach – Our research is customized to meet client-specific needs, ensuring relevant and practical recommendations.

Continuous Monitoring & Updates – We track market changes regularly to keep research findings up to date and aligned with the latest trends.

Transparent & Ethical Practices – We adhere to ethical research standards, ensuring unbiased data collection and reporting.

Which Regions Have the Highest Demand for Enterprise Data Warehouse (EDW) Market? 

The Enterprise Data Warehouse (EDW) Market Research Report provides a detailed examination of the Enterprise Data Warehouse (EDW) Market across various regions, highlighting the characteristics and opportunities unique to each geographic area.

North America

Europe

Asia-Pacific

Latin America

The Middle East and Africa

Buy Now:https://marketstrides.com/buyNow/enterprise-data-warehouse-edw-market

Frequently Asked Questions (FAQs)

What is the expected growth rate of the Enterprise Data Warehouse (EDW) Market during the forecast period?

What factors are driving the growth of the Enterprise Data Warehouse (EDW) Market?

What are some challenges faced by the Enterprise Data Warehouse (EDW) Market ?

How is the global Enterprise Data Warehouse (EDW) Market segmented?

What regions have the largest market share in the global Enterprise Data Warehouse (EDW) Market?

About Us:

Market Strides is an international publisher and compiler of market, equity, economic, and database directories. Almost every industrial sector, as well as every industry category and subclass, is included in our vast collection. Potential futures, growth factors, market sizing, and competition analysis are all included in our market research reports. The company helps customers with due diligence, product expansion, plant setup, acquisition intelligence, and other goals by using data analytics and research.

Contact Us: sales@marketstrides.com

[neilsblog]

Growing Importance of Cloud Data Warehouses in Modern Business

[bicxo]

AI-powered Cash Flow Management for the Modern CFO

The future of finance and business analytics: How AI transforms cash flow management

Cash flow management is crucial for any business's financial health and growth, ensuring funds cover expenses, debts, and investments. Traditional manual processes were error-prone and inefficient. However, AI-powered solutions are revolutionizing finance and business analytics, empowering CFOs to master cash flow management for optimal performance.

The struggles of traditional methods

Before AI, cash flow management relied on manual processes, resulting in:

Inaccurate Forecasting: Predicting cash flow involves variables like customer payments and supply chain costs. Traditional methods often led to financial shortfalls.

Limited Visibility: Integrating financial information across systems was challenging, leading to fragmented data and missed early warning signs.

Inefficient Receivables and Payables Management: Effective management goes beyond timely collections and payments; it involves optimizing credit terms and payment timings, often mismanaged traditionally.

Reactive Liquidity Management: Balancing operational needs with growth opportunities required nuanced liquidity management, difficult without AI.

AI: The game changer in cash flow management

AI-powered solutions offer significant advancements:

Automated and Accurate Forecasts: AI analyzes vast datasets, generating accurate cash flow forecasts, enabling better planning and avoiding financial overextension.

Real-Time Financial Visibility: AI systems integrate data across platforms, providing a real-time view of cash flow, allowing CFOs to address discrepancies and adjust strategies.

Optimized Receivables and Payables Management: AI automates processes, using predictive analytics for optimal invoice and payment timings, maximizing cash flow efficiency.

Enhanced Liquidity Management Strategies: AI offers real-time data and predictive insights, helping CFOs simulate financial scenarios and make informed decisions to maintain optimal liquidity.

How CFOs can leverage AI for cash flow mastery

AI-Driven Analytics for Sharper Forecasts: Utilize AI analytics for accurate forecasts, anticipating shortfalls or surpluses, and adjusting strategies.

Real-Time Dashboards for Unparalleled Visibility: Leverage AI-powered dashboards for a comprehensive view of financial health, centralizing monitoring and decision-making.

Automated Accounts Receivable and Payable Management: Automate the cycle with AI, improving efficiency and reducing errors.

Predictive Insights for Optimized Liquidity Management: Use AI for future capital planning, ensuring sufficient liquidity for operations and strategic initiatives.

Enhanced Risk Management: AI identifies risks like fraud or compliance issues, allowing early mitigation and informed credit decisions.

Streamlined Regulatory Compliance and Reporting: AI automates compliance and reporting processes, reducing administrative burdens and minimizing errors.

To read the full article click on this link

[hanasatoblogs]

Cloud vs On-Prem Data Warehouse: Making the Right Choice for Your Business

In today's data-driven world, businesses face a critical decision when it comes to choosing the right data warehouse solution. The debate between cloud and on-premise data warehouses has been ongoing, with each option offering distinct advantages and challenges. This article will delve into the practical differences between cloud and on-premise data warehouses, offering real-world examples and data-driven insights to help you make an informed decision.

What is a Cloud Data Warehouse?

A cloud data warehouse is a scalable and flexible data storage solution hosted on cloud platforms like AWS, Google Cloud, or Microsoft Azure. Unlike traditional on-premise data warehouses, cloud data warehouses eliminate the need for physical infrastructure, offering businesses the ability to store and manage data with ease and efficiency.

On-Premise Data Warehouse: A Legacy Approach

An on-premise data warehouse is a traditional data storage solution where the data is hosted on local servers within a company's own data center. This model offers complete control over the data and the infrastructure but comes with significant upfront costs and ongoing maintenance requirements.

Key Differences Between Cloud and On-Premise Data Warehouses

1. Cost Efficiency

Cloud Data Warehouse:

Pros: The pay-as-you-go model allows businesses to scale resources up or down based on demand, reducing unnecessary costs. There is no need for significant capital investment in hardware or software.

Cons: Long-term costs can add up if not managed properly, especially with increasing data volumes and computational needs.

On-Premise Data Warehouse:

Pros: Once the initial investment is made, ongoing costs can be more predictable. No recurring subscription fees.

Cons: High upfront costs for hardware, software, and skilled IT personnel. Ongoing maintenance, power, and cooling expenses add to the total cost of ownership (TCO).

2. Scalability

Cloud Data Warehouse:

Pros: Cloud solutions offer almost infinite scalability. Businesses can adjust their storage and processing power according to their needs without physical limitations.

Cons: Rapid scaling can lead to unexpectedly high costs if usage is not carefully monitored.

On-Premise Data Warehouse:

Pros: Customizable to specific business needs. Scaling is possible but requires additional hardware and can be time-consuming.

Cons: Scaling is limited by the physical infrastructure, often requiring significant time and financial investment.

3. Performance

Cloud Data Warehouse:

Pros: Advanced cloud architectures are optimized for performance, offering faster query processing and better data handling capabilities.

Cons: Performance can be affected by network latency and bandwidth limitations.

On-Premise Data Warehouse:

Pros: Performance is highly controlled, with low latency since data is processed on-site.

Cons: Performance improvements require hardware upgrades, which can be costly and time-consuming.

4. Security and Compliance

Cloud Data Warehouse:

Pros: Leading cloud providers offer robust security features, including encryption, access controls, and compliance with industry standards like GDPR, HIPAA, and SOC 2.

Cons: Data security in the cloud is a shared responsibility. Organizations must ensure that they implement proper security measures on their end.

On-Premise Data Warehouse:

Pros: Complete control over security policies and compliance with regulatory requirements. Data remains within the company's own environment.

Cons: Higher responsibility for maintaining security, requiring dedicated IT staff and resources.

Live Examples: Cloud vs On-Premise in Action

Cloud Data Warehouse: Netflix

Netflix is a prime example of a company leveraging cloud data warehouses to manage its massive data volumes. By using AWS Redshift, Netflix can analyze petabytes of data in real-time, optimizing its recommendation algorithms and improving user experience. The scalability and performance of cloud data warehouses allow Netflix to handle peak loads, such as during new content releases, without compromising speed or reliability.

On-Premise Data Warehouse: Bank of America

Bank of America relies on an on-premise data warehouse to maintain full control over its sensitive financial data. By keeping data in-house, the bank ensures that all security and compliance requirements are met without relying on external cloud providers. While the costs and complexity of managing an on-premise solution are higher, the bank prioritizes control and security over the flexibility offered by cloud solutions.

Data-Driven Insights: Market Trends and Future Outlook

Market Growth: According to a report by MarketsandMarkets, the global cloud data warehouse market is expected to grow from $4.7 billion in 2021 to $12.9 billion by 2026, at a CAGR of 23.8%. This growth is driven by the increasing adoption of cloud technologies, the need for real-time analytics, and the flexibility offered by cloud solutions.

Hybrid Approaches: Many organizations are adopting hybrid models, combining both cloud and on-premise data warehouses to balance the benefits of both. For instance, sensitive data may be stored on-premise, while less critical data is managed in the cloud.

AI and Machine Learning Integration: Cloud data warehouses are increasingly integrating AI and machine learning tools to enhance data processing capabilities. This trend is expected to accelerate, with cloud providers offering more advanced analytics and automation features.

Making the Right Choice: Key Considerations

Business Needs: Assess your organization’s specific needs, including data volume, security requirements, budget, and long-term goals.

Total Cost of Ownership (TCO): Consider both the short-term and long-term costs associated with each solution, including maintenance, upgrades, and scalability.

Security and Compliance: Ensure that your chosen solution meets all regulatory requirements and provides the necessary security features to protect your data.

Scalability and Performance: Evaluate the scalability and performance needs of your organization, and choose a solution that can grow with your business.

Conclusion

Choosing between a cloud and an on-premise data warehouse is a decision that requires careful consideration of various factors, including cost, scalability, performance, and security. While cloud data warehouses offer flexibility, scalability, and advanced analytics, on-premise solutions provide greater control and security. By understanding your organization’s unique needs and long-term goals, you can make an informed decision that will support your data management strategy for years to come.

[rajaniesh]

Implementing Data Mesh on Databricks: Harmonized and Hub & Spoke Approaches

Explore the Harmonized and Hub & Spoke Data Mesh models on Databricks. Enhance data management with autonomous yet integrated domains and central governance. Perfect for diverse organizational needs and scalable solutions. #DataMesh #Databricks

[kamogryadeshi]

❗Russian massive drone attack in Kharkiv: fires broke out, people are injured

On the evening of April 8, the Russians launched a massive attack with drones on the Kyivskyi, Industrialnyi and Osnovyanskyi districts of the city.

The hits were on civilian enterprises, warehouses and production buildings and service stations. There were 6 fire centers. The total area of the fire is over 1200 square meters.

According to preliminary data, 2 people were injured.

Units of the State Emergency Service, the National Police, doctors and municipal services are working at the scene.

[marta-bee]

News of the Day 6/11/25: AI

Paywall free.

More seriously, from the NY Times:

"For Some Recent Graduates, the A.I. Job Apocalypse May Already Be Here" (Paywall Free)

You can see hints of this in the economic data. Unemployment for recent college graduates has jumped to an unusually high 5.8 percent in recent months, and the Federal Reserve Bank of New York recently warned that the employment situation for these workers had “deteriorated noticeably.” Oxford Economics, a research firm that studies labor markets, found that unemployment for recent graduates was heavily concentrated in technical fields like finance and computer science, where A.I. has made faster gains. [...] Using A.I. to automate white-collar jobs has been a dream among executives for years. (I heard them fantasizing about it in Davos back in 2019.) But until recently, the technology simply wasn’t good enough. You could use A.I. to automate some routine back-office tasks — and many companies did — but when it came to the more complex and technical parts of many jobs, A.I. couldn’t hold a candle to humans. That is starting to change, especially in fields, such as software engineering, where there are clear markers of success and failure. (Such as: Does the code work or not?) In these fields, A.I. systems can be trained using a trial-and-error process known as reinforcement learning to perform complex sequences of actions on their own. Eventually, they can become competent at carrying out tasks that would take human workers hours or days to complete.

I've been hearing my whole life how automation was coming for all our jobs. First it was giant robots replacing big burly men on factory assembly lines. Now it seems to be increasingly sophisticated bits of code coming after paper-movers like me. I'm not sure we're there yet, quite, but the NYT piece does make a compelling argument that we're getting close.

The real question is, why is this a bad thing? And the obvious answer is people need to support themselves, and every job cut is one less person who can do that. But what I really mean is, if we can get the outputs we need to live well with one less person having to put in a day's work to get there, what does it say about us that we haven't worked out a way to make that a good thing?

Put another way, how come we haven't worked out a better way to share resources and get everyone what they need to thrive when we honestly don't need as much labor-hours for them to "earn" it as we once did?

I don't have the solution, but if some enterprising progressive politician wants to get on that, they could do worse. I keep hearing how Democrats need bold new ideas directed to helping the working class.

More on the Coming AI-Job-Pocalypse

I’m a LinkedIn Executive. I See the Bottom Rung of the Career Ladder Breaking. (X)

Paul Krugman: “What Deindustrialization Can Teach Us About The Effects of AI on Workers” (X)

How AI agents are transforming work—and why human talent still matters (X)

AI agents will do programmers' grunt work (X)

At Amazon, Some Coders Say Their Jobs Have Begun to Resemble Warehouse Work (X)

Why Esther Perel is going all in on saving the American workforce in the age of AI

Junior analysts, beware: Your coveted and cushy entry-level Wall Street jobs may soon be eliminated by AI (X)

The biggest barrier to AI adoption in the business world isn’t tech – it’s user confidence  (X)

Experts predicted that artificial intelligence would steal radiology jobs. But at the Mayo Clinic, the technology has been more friend than foe. (X)

AI Will Devastate the Future of Work. But Only If We Let It (X)

AI in the workplace is nearly 3 times more likely to take a woman’s job as a man’s, UN report finds (X)

Klarna CEO predicts AI-driven job displacement will cause a recession (X)

& on AI Generally

19th-century Catholic teachings, 21st-century tech: How concerns about AI guided Pope Leo’s choice of name (X)

Will the Humanities Survive Artificial Intelligence? (X)

Two Paths for A.I. (X)

The Danger of Outsourcing Our Brains: Counting on AI to learn for us makes humans boring, awkward, and gullible. (X)

AI Is a Weapon Pointed at America. Our Best Defense Is Education. (X)

The Trump administration has asked artificial intelligence publishers to rebalance what it considers to be 'ideological bias' around actions like protecting minorities and banning hateful content. (X)

What is Google even for anymore? (X)

AI can spontaneously develop human-like communication, study finds

AI Didn’t Invent Desire, But It’s Rewiring Human Sex And Intimacy (X)

Mark Zuckerberg Wants AI to Solve America’s Loneliness Crisis. It Won’t. (X)

The growing environmental impact of AI data centers’ energy demands

Tesla Is Launching Robotaxis in Austin. Safety Advocates Are Concerned (X)

The One Big Beautiful Bill Act would ban states from regulating AI (X)

& on the Job-Pocalypse & Other Labor-Related Shenanigans Generally, Too

What Unions Face With Trump EOs (X)

AI may be exposing jobseekers to discrimination. Here’s how we could better protect them (X)

Jamie Dimon says he’s not against remote workers—but they ‘will not tell JPMorgan what to do’  (X)

Direct-selling schemes are considered fringe businesses, but their values have bled into the national economy. (X)

Are you "functionally unemployed"? Here's what the unemployment rate doesn't show. (X)

Being monitored at work? A new report calls for tougher workplace surveillance controls  (X)

Josh Hawley and the Republican Effort to Love Labor (X)

Karl Marx’s American Boom (X)

Hiring slows in U.S. amid uncertainty over Trump’s trade wars

Vanishing immigration is the ‘real story’ for the economy and a bigger supply shock than tariffs, analyst says (X)

[raurquiz]

#Happybirthday @BrentSpiner #BrentSpiner #actor #Data #lore #b4 #ariksoong #altansoong #startrek #thenextgeneration #generations #firstcontact #insurrection #nemesis #enterprise #startrekpicard #IndependenceDay #Resurgence #outcast #supernatural #warehouse13 #startrek57