AI comes for the job market, security, and prosperity: The Debrief

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AI comes for the job market, security, and prosperity: The Debrief

When I picked up my daughter from summer camp, we settled in for an eight-hour drive through the Appalachian mountains, heading from North Carolina to her grandparents’ home in Kentucky. With little to no cell service for much of the drive, we enjoyed the rare opportunity to have a long, thoughtful conversation, uninterrupted by devices. The subject, naturally, turned to AI. “No one my age wants AI. No one is excited about it,” she told me of her high-school-age peers. Why not? I asked. “Because,” she replied, “it seems like all the jobs we thought we wanted to do are going to go away.” I was struck by her pessimism, which she told me was shared by friends from California to Georgia to New Hampshire. In an already fragile world, one increasingly beset by climate change and the breakdown of the international order, AI looms in the background, threatening young people’s ability to secure a prosperous future. It’s an understandable concern. Just a few days before our drive, OpenAI CEO Sam Altman was telling the US Federal Reserve’s board of governors that AI agents will leave entire job categories “just like totally, totally gone.” Anthropic CEO Dario Amodei told Axios he believes AI will wipe out half of all entry-level white-collar jobs in the next five years. Amazon CEO Andy Jassy said the company will eliminate jobs in favor of AI agents in the coming years. Shopify CEO Tobi Lütke told staff they had to prove that new roles couldn’t be done by AI before making a hire. And the view is not limited to tech. Jim Farley, the CEO of Ford, recently said he expects AI to replace half of all white-collar jobs in the US. These are no longer mere theoretical projections. There is already evidence that AI is affecting employment. Hiring of new grads is down, for example, in sectors like tech and finance. While that is not entirely due to AI, the technology is almost certainly playing a role. For Gen Z, the issue is broader than employment. It also touches on another massive generational challenge: climate change. AI is computationally intensive and requires massive data centers. Huge complexes have already been built all across the country, from Virginia in the east to Nevada in the west. That buildout is only going to accelerate as companies race to be first to create superintelligence. Meta and OpenAI have announced plans for data centers that will require five gigawatts of power just for their computing—enough to power the entire state of Maine in the summertime. It’s very likely that utilities will turn to natural gas to power these facilities; some already have. That means more carbon dioxide emissions for an already warming world. Data centers also require vast amounts of water. There are communities right now that are literally running out of water because it’s being taken by nearby data centers, even as climate change makes that resource more scarce. Proponents argue that AI will make the grid more efficient, that it will help us achieve technological breakthroughs leading to cleaner energy sources and, I don’t know, more butterflies and bumblebees? But xAI is belching CO2 into the Memphis skies from its methane-fueled generators right now. Google’s electricity demand and emissions are skyrocketing today. Things would be different, my daughter told me, if it were obviously useful. But for much of her generation, she argued, it’s a looming threat with ample costs and no obvious utility: “It’s not good for research because it’s not highly accurate. You can’t use it for writing because it’s banned—and people get zeros on papers who haven’t even used it because of AI detectors. And it seems like it’s going to take all the good jobs. One teacher told us we’re all going to be janitors.” It would be naïve to think we are going back to a world without AI. We’re not. And yet there are other urgent problems that we need to address to build security and prosperity for coming generations. This September/October issue is about our attempts to make the world more secure. From missiles. From asteroids. From the unknown. From threats both existential and trivial. We’re also introducing three new columns in this issue, from some of our leading writers: The Algorithm, which covers AI; The Checkup, on biotech; and The Spark, on energy and climate. You’ll see these in future issues, and you can also subscribe online to get them in your inbox every week. Stay safe out there.

“No one my age wants AI. No one is excited about it,” she told me of her high-school-age peers. Why not? I asked. “Because,” she replied, “it seems like all the jobs we thought we wanted to do are going to go away.”

I was struck by her pessimism, which she told me was shared by friends from California to Georgia to New Hampshire. In an already fragile world, one increasingly beset by climate change and the breakdown of the international order, AI looms in the background, threatening young people’s ability to secure a prosperous future.

It’s an understandable concern. Just a few days before our drive, OpenAI CEO Sam Altman was telling the US Federal Reserve’s board of governors that AI agents will leave entire job categories “just like totally, totally gone.” Anthropic CEO Dario Amodei told Axios he believes AI will wipe out half of all entry-level white-collar jobs in the next five years. Amazon CEO Andy Jassy said the company will eliminate jobs in favor of AI agents in the coming years. Shopify CEO Tobi Lütke told staff they had to prove that new roles couldn’t be done by AI before making a hire. And the view is not limited to tech. Jim Farley, the CEO of Ford, recently said he expects AI to replace half of all white-collar jobs in the US.

These are no longer mere theoretical projections. There is already evidence that AI is affecting employment. Hiring of new grads is down, for example, in sectors like tech and finance. While that is not entirely due to AI, the technology is almost certainly playing a role.

For Gen Z, the issue is broader than employment. It also touches on another massive generational challenge: climate change. AI is computationally intensive and requires massive data centers. Huge complexes have already been built all across the country, from Virginia in the east to Nevada in the west. That buildout is only going to accelerate as companies race to be first to create superintelligence. Meta and OpenAI have announced plans for data centers that will require five gigawatts of power just for their computing—enough to power the entire state of Maine in the summertime.

It’s very likely that utilities will turn to natural gas to power these facilities; some already have. That means more carbon dioxide emissions for an already warming world. Data centers also require vast amounts of water. There are communities right now that are literally running out of water because it’s being taken by nearby data centers, even as climate change makes that resource more scarce.

Proponents argue that AI will make the grid more efficient, that it will help us achieve technological breakthroughs leading to cleaner energy sources and, I don’t know, more butterflies and bumblebees? But xAI is belching CO₂ into the Memphis skies from its methane-fueled generators right now. Google’s electricity demand and emissions are skyrocketing today.

Things would be different, my daughter told me, if it were obviously useful. But for much of her generation, she argued, it’s a looming threat with ample costs and no obvious utility: “It’s not good for research because it’s not highly accurate. You can’t use it for writing because it’s banned—and people get zeros on papers who haven’t even used it because of AI detectors. And it seems like it’s going to take all the good jobs. One teacher told us we’re all going to be janitors.”

It would be naïve to think we are going back to a world without AI. We’re not. And yet there are other urgent problems that we need to address to build security and prosperity for coming generations. This September/October issue is about our attempts to make the world more secure. From missiles. From asteroids. From the unknown. From threats both existential and trivial.

We’re also introducing three new columns in this issue, from some of our leading writers: The Algorithm, which covers AI; The Checkup, on biotech; and The Spark, on energy and climate. You’ll see these in future issues, and you can also subscribe online to get them in your inbox every week.

Stay safe out there.

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Spotlight report: IT careers in the AI era

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MISO’s fast-track interconnection review draws 26.6 GW in proposals, dominated by gas

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Fed’s utility-scale storage outlook ticks upward post-OBBBA

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GasBuddy Reveals 2025 Labor Day USA Gasoline Price Forecast

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Phoenix Energy More Than Doubles Profit

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HPE extends Juniper’s Mist AI to boost data center management

Further, Aaron stated that Marvis Actions offers automated remediations for IT-approved scenarios. Using a Human-in-the-Loop (HITL) trust model, customers can develop confidence over time, giving Marvis AI Assistant permission to automatically resolve problems such as: Correcting VLAN misconfigurations Shutting down ports to resolve network loops Upgrading noncompliant devices Handling routine policy updates and firmware compliance Resolving port-stuck issues and misconfigured access points “Each action, whether initiated by IT or executed autonomously by Marvis AI Assistant, is validated post-remediation and logged in the Marvis Actions Dashboard. This maintains full auditability and HITL oversight while building trust through consistent, accurate results,” Aaron wrote. Juniper also extended Marvis further into the vendor’s Apstra data center networking environment by letting the platform have access to Apstra’s contextual graph database, which maps the components in the data center including switches, routers, servers, links, policies and services. The idea is to let the MistAI framework understand complex queries, break them into logical components, and iteratively query data sources to synthesize actionable responses, Aaron stated. “This framework currently supports nearly 300 API queries. It will expand to enable autonomous service provisioning activities, incorporate additional data sources like elastic search, and enhance feedback mechanisms for continuous learning—critical steps toward fully self-driving data centers,” Aaron wrote. In addition to the Apstra extension, Juniper is adding Marvis Minis capabilities to data center operations. Marvis Minis set up a digital twin of a customer’s network environment to simulate and test user connections, validate network configurations, and find/detect problems without users being present and without requiring any additional hardware, according to Juniper.

Vertiv launches one-day installation package for AI data center systems

Data center infrastructure vendor Vertiv has introduced Vertiv OneCore, a fully modular data center building block design supporting AI and HPC applications intended to speed deployment of compute equipment in as little as one day. OneCore is a unified “slab-up,” factory-assembled, turnkey platform that integrates the company’s power, thermal, and IT infrastructure systems within a pre-assembled shell. Slab-up is a data center design where equipment—such as server racks and cabinets—are installed directly on a solid concrete slab floor, rather than on a raised floor system. Vertiv says the design simplifies logistics, minimizes on-site labor and complexity, and supports consistent quality, cost, and schedule outcomes. Vertiv Unify, which assists Vertiv cooling, UPS, and power management equipment to connect to building and data center management, provides integrated system visibility and centralized management.

Nvidia turns to software to speed up its data center networking hardware for AI

Typically chunks of AI tasks are distributed across GPUs, which then coordinate to provide a unified output. Adaptive routing ensures the network and GPUs over long distances are in sync when running AI workloads, Shainer said. Jitter bugs “If I retransmit the packet, I create jitter, which means one GPU out of many will be delayed and all the others have to wait for that GPU to finish,” Shainer said. The congestion control improvements remove bottlenecks by balancing transmissions across switches. Nvidia tested XGS algorithms in its server hardware and measured a 1.9x improvement in GPU-to-GPU communication compared to off-the-shelf networking technology, executives said during a briefing on the technology. Cloud providers already have long-distance high-speed networks. For example, Google’s large-scale Jupiter network uses optical switching for fast communications between its AI chips, which are called TPUs. It is important to separate the physical infrastructure from the software algorithms like XGS, Shainer said.

Fluke Networks expands testing to help ease data center networking challenges

High-density fiber connections amplify contamination risks The shift toward higher-density fiber connections has significantly complicated contamination control. Modern array connectors can house up to 24 individual fibers within a single connection point. In contrast, traditional duplex connections contained just two fibers. “The slightest little bit of dust on one of those nine micron wide fibers, which, by the way, is much smaller than a human hair, the slightest little bit of dust on any one of the 24 in that connector, and it’s not going to work,” Mullins explained. The inspection and cleaning requirements extend beyond traditional fiber testing. Each kit includes cleaning and inspection capabilities. Mullins noted that many technicians take shortcuts on fiber preparation. “Cleaning and inspecting a fiber, every time you unplug it and plug it back in, adds, like another minute worth of work. But you know what? If you don’t do it, you’re gonna pay for it down the road,” he said. Cable identification a persistent challenge In addition to the new kits, Fluke Networks is also continuing to help solve other persistent networking issues. Physical cable identification continues to plague data center operations despite advances in network management and monitoring. Fluke’s solutions address this through multiple approaches. These include tone and probe technology, remote identification systems, and active network port discovery.

Cisco ties storage networking gear to IBM z17 mainframe

“IBM Z systems are mainframes known for their ability to handle massive transaction volumes, support large-scale databases, and provide unmatched levels of security and uptime,” wrote Fausto Vaninetti, a senior solutions engineer for data center technologies at Cisco, in a blog post about the news. “The newest in the IBM Z system family, IBM z17 is the first mainframe fully engineered for the AI age, unlocking expanded capabilities for enterprise-scale AI, such as large language models, generative AI, and accelerated inferencing. However, the performance of mainframe applications depends on the underlying storage infrastructure.” SANs play a critical role in ensuring fast, reliable, and secure access to data, Vaninetti wrote: “For mainframe environments, which leverage high-speed [Fibre Connection] FICON protocol, having a robust SAN fabric that supports these requirements is non-negotiable. A solution that combines high throughput, low latency, and enterprise-class resilience is vital to ensure seamless operations and meet stringent service-level agreement requirements.” According to Vaninetti, some standout features of the MDS 9000 Series for mainframe environments include:

Scaling Up: Tract’s Master-Planned Land and Infrastructure Approach to Data Center Development

With the rapid growth of physical data center infrastructure, it’s no surprise that a niche market has emerged for companies specializing in land acquisition. Reports of massive property purchases by firms planning new facilities appear almost daily—and so do accounts of the challenges developers face before the first shovel hits the ground. As parcel sizes grow and power and water demands intensify, the complexities of acquiring and preparing these sites have only increased. Tract is a leader in this space. The Denver-based company develops master-planned data center parks, with more than 25,000 acres of potential sites under its control and plans to support over 25 GW of workload capacity. To put that into perspective, 25,000 acres is roughly 40 square miles—about two-thirds the land area of Washington, D.C., or, for European readers, two-thirds the size of Liechtenstein. Building Shovel-Ready Megasites Rather than waiting for developers to come knocking, Tract takes a proactive approach, built on the core belief that the future of data center growth lies in pre-entitled, zoned, and infrastructure-ready megasites. The company works years in advance to deliver shovel-ready campuses with reliable energy, fiber connectivity, and municipal cooperation already in place. Its model emphasizes strategic land aggregation in high-growth regions, the cultivation of long-term relationships with utilities and governments, and master planning for power, cooling, transportation, and sustainability. This integrated approach positions Tract to deliver both speed and certainty to hyperscale project developers—at scale. Tract’s leadership team brings deep industry experience. Founder and Executive Chairman Grant van Rooyen previously led acquisitions and expansions at Cologix and Teraco. President Matt Spencer brings more than 35 years of telecom and infrastructure leadership, while Chief Energy Officer Nat Sahlstrom, former head of Amazon’s global energy, water, and sustainability teams, helped make Amazon the world’s largest buyer of renewable energy. Backed by

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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