Impact of Automation

🤖 The European Union needs to rapidly upskill its citizens if it's going to capitalise on the benefits that artificial intelligence can bring, according to a report from LinkedIn's Economic Graph team. AI has been hailed as a technology that can help humans with everything from boosting office productivity to drug discovery. But a lack of talent is one of the biggest hurdles. 🗒 AI talent makes up just 0.41% of EU workers, LinkedIn's report, AI in the EU, found. While that's a 126% increase on 2016, and more than the UK (0.35%) and the US (0.34%), the bloc still needs more people who know how to get the most out of the technology. 📍 As it stands, just 26.3% of the EU's AI talent is female, which is less than the UK (27.7%) and the US (29.8%). It will take 162 years to reach gender parity if the gap keeps on closing at the current rate, according to the report. Addressing the gender imbalance in AI is one way the EU could try and close the skills gap, according to the report. In terms of AI's impact on the workforce, women are likely to be disproportionately impacted by AI, and generative AI (gen AI) in particular, which is capable of creating a variety of content including emails and presentations. Gen AI is poised to impact a number of jobs that tend to be held by women including medical clerks, clinical research assistants and sales operations assistants. 🗣️ What’s your take on these findings? Are you aware of AI’s impact and its presence within the EU workforce? We’d love to hear your thoughts in the comments. Full report: https://lnkd.in/g3_EhhiP 🖊️ Sam Shead  📸 Getty Images #AIInTheEU

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

AI adoption is accelerating faster than the energy systems built to support it. Data centers are already among the most power-intensive assets on the grid and are seeing demand rise at rates that legacy infrastructure, static operating models, and fragmented regional grids were simply not designed to handle. The consequence is predictable: higher costs, growing emissions, and mounting pressure on utilities and operators trying to maintain reliability while integrating renewables. I’ve spent much of my career working at the intersection of technology, energy policy, and industrial systems, and this challenge is proving to be one of the defining infrastructure questions of the decade. It’s increasingly clear that the sector needs new ways to manage load, forecast demand, and coordinate resources across highly variable conditions. This week, I had the opportunity to hear from senior leaders at Hanwha Qcells about a model they are developing that aims to address these pressures. What stood out to me was the architectural shift behind the technology: using AI, interoperable language, and digital twins to unify diverse equipment, link operations to real-time grid signals, and automate many of the repetitive, checklist-style decisions that currently consume operator time. This broader concept of treating data centers as intelligent, grid-aware assets aligns with conversations happening across industry and government. The framework they described integrates clean generation, storage, and control software into a single adaptive system. The goal is straightforward but ambitious: reduce wasted energy, cut emissions, and improve resilience as AI demand grows. Their lofty projections (20–30% cost reductions, up to 35% emissions cuts, faster response times through agentic operations) reflect why approaches like this are gaining momentum. What interests me most is how these ideas fit into the larger trend: the shift toward an “Intelligent Age” where digital growth and energy management are inseparable... remember when VPPs were unheard of? Solutions that improve transparency, interoperability, and operational flexibility will be essential, and not just for data centers, but for manufacturing, transportation, and other power-intensive sectors facing similar constraints. As we look ahead, the real opportunity is in building systems that scale, adapt, and operate with far greater situational awareness. The conversation with Qcells underscored how quickly this space is evolving and why collaboration across utilities, technology developers, operators, and policymakers will be critical in the years ahead. Article link: https://bit.ly/4qggMLd #Hanwha | #HanwhaQcells | #Microsoft | #AI | #DataCenters | #EnergyManagement | #GridModernization | #CleanEnergy | #Innovation

Another job, automated. This robot lays tiles faster and more precisely than any human crew. Perfect precision. Zero breaks. 24/7 operation. The economics are compelling: ✅ 6x faster than human crews ✅ 30-40% lower labor costs ✅ Zero fatigue or injuries ✅ 15% less material waste But here's the real math: ✅ Today: $150K price tag limits adoption ✅ Tomorrow: Mass production drops costs 70% ✅ Next year: Every major contractor has one The ripple effect: ✅ 1 robot = 6 displaced workers ✅ Those workers stop spending locally ✅Tax base shrinks, social costs rise ✅ Political backlash becomes regulatory risk Smart companies are asking different questions: Not "Can we automate?" but "How do we automate responsibly?" ✅ Phased implementation with retraining ✅ Partnership with trade schools ✅ Investment in complementary human skills The C-suite reality: Short-term cost savings vs. long-term ecosystem stability. Your customers, communities, and stakeholders are watching. Automation isn't the enemy. Automation without strategy is. To stay current with the latest trends in #Technology and #Innovation, Subscribe to 👉 #CXOSpiceNewsletter here https://lnkd.in/gy2RJ9xg Or 👉 #CXOSpiceYouTube here https://lnkd.in/gnMc-Vpj #Robotics #Innovation #DigitalTransformation

We made an AI judge. It oversees our new private court system. And it goes live today. Actually, my amazing co-founders Kenny McLaren and Kimo Gandall built it. We call it Arbitrus. Litigants: you no longer need to wait for years to get your dispute resolved. Sure, you could go to state or federal court, spend a lot of money, and wait 1 to 5 years to get a result. Or you could go to human arbitration, spend maybe 1/3 as much, and still wait about 9 to 12 months to get a decision. OR you can sign up to use Arbitrus today—-and get a decision in a few weeks for 1/10 the cost. This is not a joke. We’ve been working on Arbitrus for over a year. And today (on my birthday, no less) Arbitrus— the first fully private, fully automated court system — is officially open for business. Now, please, before you jump to the conclusion that LLMs can’t handle this, read the technical paper linked in the comments. Kimo, Kenny, and the team ran 100 different scenarios through Arbitrus last month. Harvard law students graded the outputs. There were no hallucinations and 98 out of the 100 Arbitrus rulings had no issues at all. The Arbitrus’ team has written everything up in a comprehensive open-sourced 80+ page paper that will be published in a prominent law journal soon. (It’s on SSRN right now.)   Of course, we don’t expect a lot of litigants fighting over millions of dollars to use Arbitrus right out of the gate. But there are a lot of immediate use cases now for people, companies, and governments. Low-dollar two party contracts, like vendor and landlord/tenant agreements, for example, can now include Arbitrus as the dispute resolution mechanism. Then if a dispute happens, the parties will get a cheaper, quicker, and likely more impartial answer than using human arbitration, which would otherwise likely cost more than the dispute itself is even worth. I know that for some of you it will feel weird to let a computer decide. But we’ve been letting computers decide outcomes for decades. Arbitrus is just more transparent about it. Pandora and Spotify pick your music. TikTok and YouTube pick what you watch. Google Maps tells you where to go. It’s pretty widely accepted that computers are great at processing, interpreting, and summarizing large amounts of data. In our legal system, who does that? It’s not the lawyers. Lawyers persuade and negotiate. That’s more difficult for a computer to do. It’s the judges, arbitrators, administrative hearing officers, Human Resources departments, and other such decision-makers that perform this function. These fine folks read and interpret the law (i.e., large amounts of data), weigh the credibility of the evidence, and then apply the facts to the law. Arbitrus can do that now. There’s a new court system in town. Get ready. Go to Arbitrus Dot AI to learn more. 

AI is changing the economics and speed of cyberattacks. What once took threat actors days or weeks can now happen in minutes: automated reconnaissance, AI-assisted exploit development, credential targeting, lateral movement, and highly personalized phishing at scale. This is why Palo Alto Networks believes so strongly in the concept of autonomous resilience. The traditional model of security operations: fragmented tools, manual escalation paths, and human-speed response cycles - was not designed for machine-speed threats. Autonomous resilience means building security architectures that can continuously reduce exposure, validate trust, and contain threats in real time. What does that look like in practice? 🔸 Minimize attack surface Continuously identify and remediate exposed assets, misconfigurations, vulnerable APIs, and unmanaged cloud resources before attackers can weaponize them. For example, AI-driven exposure management can detect an internet-facing development environment created outside policy and trigger automated remediation immediately. 🔸 Secure every identity Trust must extend beyond employees to machine identities, workloads, APIs, and AI agents. This means enforcing least privilege, adaptive access controls, and continuous identity validation to stop credential misuse and token theft before attackers gain persistence. 🔸 Defend the software supply chain AI-assisted attacks increasingly target CI/CD pipelines, open-source dependencies, and code repositories. Organizations need runtime protections, code integrity validation, and automated policy enforcement to prevent manipulated code from reaching production environments. 🔸 Constrain blast radius Zero Trust architectures become even more critical in an AI-driven threat landscape. Microsegmentation, continuous inspection, and behavioral analytics help prevent attackers from moving laterally across environments once initial access is achieved. 🔸 Detect and respond in real time Security teams cannot rely on analysts manually correlating thousands of alerts. AI-driven SOC operations can automatically prioritize incidents, enrich telemetry, isolate compromised assets, and initiate containment workflows within minutes — dramatically reducing operational fatigue and response time. The outcome is not “fully autonomous security.” The outcome is resilient organizations that can adapt, contain, and recover faster in an increasingly automated threat environment. Cybersecurity is evolving from reactive defense into continuous operational resilience. The organizations preparing for that shift now will be far better positioned for what comes next.

"When automation removes the simpler tasks (as accounting software did for bookkeeping clerks), the remaining work becomes more specialized, wages rise, and fewer workers qualify. When it removes the harder tasks (as inventory management systems did for warehouse workers), the job becomes more accessible, employment expands, and wages fall. Same technology, opposite labor market outcomes, depending on which part of the job gets automated." - Alex Imas, The University of Chicago Booth School of Business from What Will Be Scarce? https://lnkd.in/eqdKHk2u

Headlines about AI-driven job loss are everywhere. Anxiety is spiking. It can feel like jobs are already vanishing en mass. 𝐁𝐮𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞𝐲? (𝘚𝘱𝘰𝘪𝘭𝘦𝘳: 𝘯𝘰.) Together w/ the brilliant Martha Gimbel and her incredible The Budget Lab at Yale colleagues --Joshua Kendall and Madeline Lee-- we set out to test whether these fears match the data. In a new paper out today (link in comments), we find no evidence of major AI job displacement in the 33 months since ChatGPT's launch. The % of workers in jobs w/ high, medium, and low AI “exposure” has remained remarkably steady. Even amid rapid AI progress, the story of the labor market so far is stability, not collapse. While these findings may surprise those expecting more rapid displacement, we show they are consistent with the pace of job changes from past tech advances like the computer and internet. Why? In a The Brookings Institution post today (link in comments), we discuss the uneven pace of AI adoption across sectors and the messy reality of workplace tech adoption. So far, AI’s labor market impacts resemble the slower, uneven diffusion of past technologies, which Arvind Narayanan and Sayash Kapoor refer to as ‘AI as normal technology. Two important notes: ➡️ First, this doesn't mean AI has had 𝒏𝒐 impact on jobs at all. Our paper is consistent with emerging evidence from Erik Brynjolfsson & Bharat Chandar that AI may be contributing to unemployment among early-career workers. (It could also be consistent / evidence that a weakening labor market is hurting those same workers.) But our approach zooms out to ask whether AI is already causing economy-wide disruption—and the answer is no. ➡️ Second, these are not predictions. At any point, AI's labor market impact could accelerate, or not. The future requires vigilance. That is why we will continue to monitor these changes monthly. (Be sure to follow The Budget Lab at Yale for more.) But vigilance also requires better data. Anthropicic has led in transparently sharing Claude usage data, an OpenAI has recently shared ChatGPT usage stats. But these offer only a partial view. To truly understand AI’s trajectory, Google, Microsoft, OpenAI, etc should share usage data at both individual and enterprise level. Without this, we are flying blind into one of the most significant technological shifts of our time. Huge thx to Claire Jones for the great Financial Times coverage today. And enormous thx to Martha, Josh, Maddie and the Budget Lab team for an incredibly fun collaboration. Ben Harris Sanjay Patnaik Mark Muro Joshua Gans Nicholas Thompson Simon Johnson Anton Korinek Adrian Brown Anmol Chaddha Michael Kubzansky Bharat Ramamurti Stephanie Bell Ritse Erumi Ellie Bertani Michael Belinsky Alex Tamkin Pamela M. Ajay Agrawal Andrew Sweet Rachel Korberg Zoë Hitzig David Deming Peter McCrory Kevin Delaney Heather Long

NSW just made AI a WHS issue. Not privacy. Not IT. Safety. Last week Parliament passed amendments to the Work Health and Safety Act 2011 (NSW) regulating “digital work systems” meaning AI, algorithms, automation and online platforms used to allocate or monitor work. If software is setting shifts, tracking performance, allocating tasks or nudging productivity, there is now an explicit duty to ensure those systems do not create health and safety risks. The law specifically calls out excessive workloads, unreasonable performance metrics, excessive monitoring and discriminatory decision making. That framing matters. If an algorithm creates unsafe pressure, it is no longer just an employee relations issue. It sits inside your primary duty of care. There is also expanded right of entry. WHS permit holders can require access to inspect digital systems when investigating suspected breaches. Code, performance metrics, data logs and audit trails are all in scope, subject to notice and guidelines. This is a material shift. For years digital transformation has been sold as efficiency. NSW has reframed it as risk and governance. If you are rolling out AI in workforce management, the question is no longer just “does it work?” It is “can we defend it as safe and reasonable?” Feels like the start of a broader national conversation.