<?xml version="1.0" encoding="utf-8"?> <rss version="2.0"> <channel> <title>Remote IT Jobs | Find Remote Tech Jobs Worldwide</title> <link>https://www.remoteitjobs.app</link> <description>Discover top remote IT jobs from leading tech companies. Search software development, DevOps, cybersecurity, and tech leadership positions. Apply to work-from-home tech jobs today.</description> <lastBuildDate>Sat, 17 Oct 2026 09:11:34 GMT</lastBuildDate> <docs>https://validator.w3.org/feed/docs/rss2.html</docs> <generator>https://github.com/jpmonette/feed</generator> <language>en</language> <image> <title>Remote IT Jobs | Find Remote Tech Jobs Worldwide</title> <url>https://www.remoteitjobs.app/images/logo-512.png</url> <link>https://www.remoteitjobs.app</link> </image> <copyright>All rights reserved 2024, RemoteITJobs.app</copyright> <category>Bitcoin News</category> <item> <title><![CDATA[Data Center Backlash Could Cost 2 Million Jobs and $294 Billion—Here’s Why]]></title> <link>https://www.remoteitjobs.app/article/data-center-backlash-could-cost-2-million-jobs-and-294-billion-here-s-why</link> <guid>data-center-backlash-could-cost-2-million-jobs-and-294-billion-here-s-why</guid> <pubDate>Fri, 16 Oct 2026 19:37:20 GMT</pubDate> <description>< ## The Cost of Opposition As countries around the world race to develop and deploy AI, the United States risks undermining its competitive advantage by increasingly resisting the infrastructure needed to support it. According to **Heatmap data**, at least **$260 billion** in planned U.S. data center investments have reportedly been canceled following local opposition in 2026 alone, while approximately **$1 trillion** in additional investments face some form of community resistance. Such opposition reflects broader public skepticism toward data center development. According to an **ITIF and Public First survey**, nearly half of Americans (**46 percent**) oppose new data center developments in their communities, and barely a quarter (**26 percent**) support them. Similarly, a **Pew Research Center survey** found that **60 percent** of Americans say they would be not too comfortable or not at all comfortable with a new data center operating in their community. This growing resistance is particularly concerning as **China continues to invest heavily** in AI infrastructure and **narrow the technological gap** with the United States. By making it increasingly difficult to build the computing capacity necessary for AI development, the United States risks constraining AI adoption, slowing productivity growth, and weakening its long-term economic competitiveness. ## Economic Impact: Jobs, Wages, and Tax Revenue The economic consequences of canceled data center investments extend far beyond the technology sector. To quantify these potential losses, this analysis uses existing estimates of the economic contributions of large data centers to calculate the impacts of canceled investments. Based on these calculations, the **$260 billion in canceled investments in 2026** represents the equivalent of approximately **1,207 foregone data center projects**. During the construction phase, which typically lasts between 18 and 24 months, these projects could have supported **more than 2 million jobs** and generated approximately **$94 billion in wages**, **$294 billion in local economic activity**, and **$12 billion in state and local tax revenues**. (See Figure 1.) These benefits would have extended across numerous industries, including construction, electrical equipment manufacturing, engineering, transportation, and other supporting services. Moreover, this economic activity would have directly benefited the communities hosting these facilities, creating employment opportunities and generating additional demand for local businesses, such as restaurants, hotels, grocery stores, and equipment rental companies.  Data centers are built to provide essential digital infrastructure, not to serve as jobs programs, and their permanent on-site workforces are relatively modest compared with the number of workers required to construct them. Nevertheless, their economic contributions persist long after the construction phase. Once operational, data centers require ongoing spending on electricity, maintenance, equipment, security, and other services, supporting employment and economic activity throughout their surrounding communities. **ITIF estimates** that the canceled projects could have supported **more than 189,000 jobs annually**, generating approximately **$9 billion in wages**, **$39 billion in local economic activity**, and **$1.3 billion in state and local tax revenues each year**. Unlike the temporary economic benefits associated with construction, these recurring contributions could have provided communities with a sustained source of employment, business activity, and public revenue. Consequently, opposition to data center development risks not only eliminating immediate construction opportunities but also depriving local economies of long-term economic benefits.  ## Balancing Concerns and Benefits Like any form of industrial development, data centers involve trade-offs, and not every project will be the right fit for every community. Concerns about electricity demand, water use, noise, and pollution deserve consideration, but their significance varies by project and location. Some data centers cover the costs of the electricity and water they consume, and in some locations, water availability is not a significant constraint. Policymakers should therefore evaluate proposed projects on their individual merits, ensuring that costs are appropriately addressed and communities benefit from the investment. But **blanket opposition** to data centers ignores these differences and risks sacrificing substantial economic benefits without regard to a project's actual costs or local impacts. The goal should be to enable responsible development, not to reject it categorically. Rather than imposing outright bans or restrictive policies that discourage data center investment, state and local policymakers should adopt a targeted approach that addresses specific risks while allowing responsible development. State utility regulators should ensure that large electricity users pay rates reflecting the costs of serving them, while encouraging data centers to **manage demand flexibly** during periods of grid stress. Policymakers should also improve grid planning and interconnection processes to accommodate new demand without unnecessarily delaying other projects. On **water**, states should require consistent reporting of consumption and focus safeguards on watersheds where supplies are genuinely constrained, using performance standards that encourage conservation rather than mandating particular technologies. Local governments can address site-specific concerns such as noise and air pollution through enforceable standards. As **ITIF has argued**, modernizing how policymakers measure, price, and manage data center impacts can protect communities and infrastructure without unnecessarily restricting investment. State and local officials should ensure that communities share in the **fiscal benefits of data center development**, using tax revenues to support public services, reduce the tax burden on residents, or invest in local infrastructure and workforce development. Communities such as **Loudoun County, Virginia**, demonstrate how data center tax revenues can strengthen local finances and help reduce property tax rates. The goal should be to ensure that residents see tangible benefits from hosting these facilities while requiring developers to address the costs their projects impose. As AI becomes increasingly central to economic growth and national competitiveness, the United States cannot afford to turn away data center investments that could generate jobs, strengthen local economies, and provide the infrastructure necessary for continued innovation. ## Methodology To estimate the economic losses associated with canceled data center projects in 2026, this analysis first obtained the total value of canceled data center investments, approximately **$260 billion**, from Heatmap. Next, using a **U.S. Chamber of Commerce study**, the analysis obtained the initial capital expenditure required to construct a typical large data center, along with estimates of the local jobs, wages, economic activity, and state and local tax revenues generated during both the construction and annual operational phases. The total value of canceled data center investments was then divided by the initial capital expenditure required for a typical large data center to estimate the number of equivalent large data center projects represented by cancellations in 2026. To estimate the economic losses associated with the construction phase, the estimated number of canceled data centers was multiplied by the estimated local jobs, wages, economic activity, and state and local tax revenues associated with constructing a typical large data center. The same approach was applied to the operational phase, multiplying the estimated number of canceled data centers by the corresponding annual employment, wage, economic activity, and state and local tax revenue estimates for a typical large data center. These estimates represent the potential economic activity foregone as a result of canceled data center investments, assuming that each canceled project would have generated economic benefits comparable to those of a typical large data center. Construction-phase estimates reflect one-time losses, while operational-phase estimates represent recurring annual economic activity that would have been generated had the projects proceeded.]]></description> <author>contact@remoteitjobs.app (RemoteITJobs.app)</author> <category>datacenters</category> <category>ai</category> <category>economy</category> <category>jobs</category> <category>techpolicy</category> <enclosure url="https://cdn.sanity.io/images/03hnmfyj/production/b87d5c413fa9fff3a44ac3c580bff25d0c2147da-1920x1080.jpg" length="0" type="image/jpg"/> </item> <item> <title><![CDATA[AI Won't Steal Jobs: 94% of NZ Accounting Firms Say No Layoffs, Here's Why]]></title> <link>https://www.remoteitjobs.app/article/ai-won-t-steal-jobs-94-of-nz-accounting-firms-say-no-layoffs-here-s-why</link> <guid>ai-won-t-steal-jobs-94-of-nz-accounting-firms-say-no-layoffs-here-s-why</guid> <pubDate>Mon, 12 Oct 2026 03:46:00 GMT</pubDate> <description><![CDATA[## AI in Accounting: A Job Creator, Not a Job Killer A new survey from Xero reveals that **94% of New Zealand accounting and bookkeeping firms do not expect AI to reduce their team sizes** in the next year. The study, which polled 526 senior professionals, suggests that AI is reshaping workloads rather than eliminating jobs. ### The Numbers Behind the Shift - **78%** of practices have experimented with AI, and **49%** now use it in daily workflows. - Firms report an average time saving of **7 hours per week** per staff member, translating to annual savings of about **AUD $145,000**. - Top-performing firms save even more: **10 hours per week** and **AUD $200,000** annually. Instead of cutting staff, firms are redirecting these time savings into **higher-value advisory services**. **42% of accountants** and **30% of bookkeepers** say they are shifting freed-up time into advisory work. > "There's been a lot of focus on whether AI will replace accounting jobs. What this research shows is a different kind of change: technology is taking pressure out of routine work, while practices are putting more time into advice, relationships and growth," said Bridget Snelling, Country Manager, Xero New Zealand. ### Hiring Shifts: New Roles Emerge AI is also changing how firms hire. **61%** of practices say AI has already altered their recruitment approach, with **35%** placing greater emphasis on soft skills and relationship management. One in five firms are now recruiting for **non-traditional roles** such as data analysts and tax technologists. ### Process Discipline: The Key to AI Success Firms with stronger process discipline are more likely to be active AI users. Among daily AI users, **89%** have well-documented core processes, compared to just **30%** of non-users. > "AI works best when it's applied to a clearly understood process, not dropped into a practice and expected to improve everything," said James Bergin, EGM of Technology Research and Advocacy at Xero. ### Advisory Services Drive Profitability The research also highlights the growing importance of advisory services. **59%** of NZ practices offer advisory, and for those firms, it's their top-earning service. Among firms not offering advisory, **27%** cite lack of capacity as the main barrier. Value-based pricing is also on the rise: **66%** of client engagements are billed on non-hourly terms, and **45%** of firms using value-based pricing say it has made them more profitable. Top-performing practices are **2.2 times more profitable** than lower-margin peers, thanks to embedded AI, higher-value services, and disciplined pricing. ### Case Study: Oxygen Advisors Oxygen Advisors, an outsourced finance function for high-growth tech companies, delivers **85%** of its work through CFO partners in strategy, capital raising, and financial decision-making. The firm has also invested in internal automation, with four technically minded accountants building tools like accounts payable automation and consolidated client summaries. > "Technology is giving accounting firms the opportunity to spend less time on repetitive work and more time helping clients make better decisions," said Matt Dold, Founder of Oxygen Advisors. ### The Bottom Line AI is not replacing accountants—it's transforming their roles. Firms that embrace AI and focus on advisory services are seeing significant time and cost savings, while also creating new opportunities for growth and specialization.]]></description> <author>contact@remoteitjobs.app (RemoteITJobs.app)</author> <category>ai</category> <category>accounting</category> <category>futureofwork</category> <category>xero</category> <category>newzealand</category> <enclosure url="https://itbrief.co.nz/uploads/story/2026/10/12/compatible_bridget-snelling-story-321134.jpg" length="0" type="image/jpg"/> </item> <item> <title><![CDATA[Tech Giants Announce Major Executive Hires: Cato Networks, Sapiens, ARIS, Azul, and More]]></title> <link>https://www.remoteitjobs.app/article/tech-giants-announce-major-executive-hires-cato-networks-sapiens-aris-azul-and-m</link> <guid>tech-giants-announce-major-executive-hires-cato-networks-sapiens-aris-azul-and-m</guid> <pubDate>Fri, 09 Oct 2026 16:02:05 GMT</pubDate> <description><]]></description> <author>contact@remoteitjobs.app (RemoteITJobs.app)</author> <category>techindustry</category> <category>executivehires</category> <category>leadership</category> <category>ai</category> <category>cloud</category> <enclosure url="https://www.iteuropa.com/sites/default/files/article/2026/Appointment-Agreement-skills-3371153_1280_21.jpg" length="0" type="image/jpg"/> </item> <item> <title><![CDATA[He Quit Oracle to Sell Bananas? Now He Earns 5x More]]></title> <link>https://www.remoteitjobs.app/article/he-quit-oracle-to-sell-bananas-now-he-earns-5x-more</link> <guid>he-quit-oracle-to-sell-bananas-now-he-earns-5x-more</guid> <pubDate>Thu, 08 Oct 2026 14:03:29 GMT</pubDate> <description><![CDATA[## From Oracle to Uber: A Leap of Faith Soham Chakraborty had it all: an engineering degree, a stable job at **Oracle** in Bengaluru, and a promising IT career. But three years in, he knew he didn't belong. "Like a lot of typical engineers, I got sucked into IT," he told Moneycontrol. "I realised that I don't quite fit in. I don't quite like what I do and I'm not very good at it either." His resignation letter sat on his computer for months before he finally quit in 2016. He had calculated his savings could last two years, including student loans. He told his father, "I'll do anything. I'll sell bananas on the street if I have to, but I won't do this because this has to have more meaning in life." His father encouraged him to take the risk. ## Driving Uber Leads to an Unexpected Career Chakraborty returned to Kolkata and drove an Uber for nearly a year. That experience opened his eyes to parts of the city he'd never seen. Meanwhile, his parents ran an Airbnb in an older part of Kolkata. Guests often wanted to explore beyond the usual tourist spots. One guest asked Chakraborty to show them around, and that one-off walk turned into a paid experience. Demand grew through word-of-mouth and reviews. That's how **Calcutta Capsule**, his tourism business, was born. He also runs Airbnb properties with his parents. ## Financial Independence and Growth The transition wasn't immediate. It took about two years to become financially independent without dipping into savings. But the gamble paid off. "What I used to earn when I was working in IT, I am making almost **four to five times** of it annually," Chakraborty says. He now handles everything from tours to videos, websites, and marketing. His tech background helps him run the business efficiently. "I like how my background in tech is fuelling my creative dreams now," he says. ## Lessons from a Career Switch His mother took years to accept his choice, but customer feedback and media coverage eventually convinced her. Chakraborty has hosted over **4,000 guests**, including more than **2,000 foreign tourists**. His advice? Don't quit just for passion. "Taking risks is important. But understanding what can actually pay back is important too." *Image: Soham Chakraborty leading a tour in Kolkata (Photo: Calcutta Capsule)*]]></description> <author>contact@remoteitjobs.app (RemoteITJobs.app)</author> <category>careerchange</category> <category>entrepreneurship</category> <category>tourism</category> <category>techindustry</category> <category>kolkata</category> <enclosure url="https://akm-img-a-in.tosshub.com/indiatoday/images/story/202610/oracle--it-job--engineer-080322602-16x9_0.png?VersionId=U8PynkPwhG7jwPWc63SbQKFuuoontbtl" length="0" type="image/png"/> </item> <item> <title><![CDATA[Google's Flexible VMs: The Secret Weapon to Keep Your Spark Jobs Alive During AI-Driven Compute Shortages]]></title> <link>https://www.remoteitjobs.app/article/google-s-flexible-vms-the-secret-weapon-to-keep-your-spark-jobs-alive-during-ai-</link> <guid>google-s-flexible-vms-the-secret-weapon-to-keep-your-spark-jobs-alive-during-ai-</guid> <pubDate>Tue, 06 Oct 2026 16:30:00 GMT</pubDate> <description><![CDATA[## Google Introduces Flexible VMs for Apache Spark Google has unveiled **flexible virtual machines** in its **Managed Service for Apache Spark** to tackle **cluster provisioning failures** during compute shortages. As AI infrastructure demand skyrockets, Spark users often face delays when their preferred machine family is unavailable. Flexible VMs let you define an **ordered list of acceptable machine families** for master, primary worker, and secondary worker nodes, ensuring your jobs keep running. ## How Flexible VMs Work Instead of tying a cluster to a single instance type, flexible VMs allow a **mix of machine types and generations** in one configuration. This includes Gen2 families like **N2 and N2D** alongside Gen4 families such as **N4 and C4**. Storage choices also adapt to the selected machine family at provisioning. A key feature is the **ranking system** that determines the order in which machine families are tried. Google recommends specifying **at least two machine families in the top-priority rank** to improve the chances of obtaining capacity during high demand. ### Example Configuration for Production Pipelines For pipelines built around `n2d-standard-16` shapes, Google suggests a tiered approach: - **Rank 0:** `n2d-standard-16` and `n2-standard-16` - **Rank 1:** `n4-standard-16` and `n4d-standard-16` - **Rank 2:** `c4-standard-16` and `c3-standard-22` - **Rank 3:** `e2-standard-16` For legacy `n1-standard-16` workloads, the ranking places `n1-standard-16` and `n2-standard-16` first, followed by `n2d-standard-16`, then `n4-standard-16` and `n4d-standard-16`, and finally `e2-standard-16`. This provides a path to newer architectures while maintaining operational continuity. ## Storage Considerations Google emphasizes **Hyperdisk Balanced** for newer instance families like **N4 and C4**, which rely on Hyperdisk for predictable performance. Default IOPS and throughput settings are likely sufficient for many distributed Spark jobs. However, if you want broader fallback options, you may need to accept a **change in disk type** when workloads move between older and newer machine families. ## Operational Trade-offs Broadening machine options introduces several considerations: - **Quotas:** You need enough compute and disk quota for every machine type and storage option listed in your flexible VM rankings, including Hyperdisk. - **Committed spending:** Traditional committed use discounts are tied to specific machine families. For savings across multiple VM families and regions, consider **compute flexible committed use discounts**. - **Performance predictability:** Results can vary between machine generations and storage options (e.g., local SSD vs. Hyperdisk). Test your Spark jobs to assess the impact on service levels. ## Broader Resilience Steps Alongside flexible VMs, Google recommends several operational measures to improve job success under constrained capacity: - **Automatic zone selection** to place jobs where resources are available. - **Favor smaller machine shapes** over heavily contested large-core instances. - **Use autoscaling** with sensible upper limits for variable workloads. - **Partial cluster creation** to start with a minimum number of primary workers and add more later. - **Regional fallback planning**, especially for high-demand areas like `us-central1`. ## The Bigger Picture As AI workloads compete with conventional analytics for the same infrastructure, Spark users must think beyond a single instance type. The question now is: **how many acceptable substitutes can be built into the configuration before a shortage leads to failure?** Flexible VMs aim to keep Spark pipelines running when regional or zonal stockouts affect a preferred machine family.]]></description> <author>contact@remoteitjobs.app (RemoteITJobs.app)</author> <category>googlecloud</category> <category>apachespark</category> <category>flexiblevms</category> <category>cloudcomputing</category> <category>bigdata</category> <enclosure url="https://itbrief.com.au/uploads/story/2026/10/05/compatible_cloud-servers-spark-cluster-shield-data-center-story-320595.jpg" length="0" type="image/jpg"/> </item> </channel> </rss>