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Supply chains move faster than any human team can track by hand. Decisions pile up every minute, variables shift without warning, and the ripple effects of a single delay can spread across your entire network. 

Manual decision-making just can’t keep pace with the speed and scale of modern operations.

Agentic AI changes that, taking basic automation from simple rule-following to truly intelligent decision-making. These autonomous agents sense what’s happening, evaluate competing priorities, and act in real time to keep your supply chain resilient and profitable. And they do it all without manual intervention — so your teams can focus on bigger strategic challenges.

Key takeaways

• Agentic AI transforms supply chains by replacing static automation with dynamic, decision-making agents that adapt in real time.
• These agents operate across procurement, logistics, forecasting, and maintenance—optimizing decisions faster and more accurately than human teams.
• Early wins come from embedding agents into repeatable processes with clean data and measurable ROI, such as demand planning or shipment rerouting.
• A successful implementation depends on a strong foundation: real-time data integration, clear governance, and trusted orchestration between agents.

How agentic AI applications are optimizing supply chains

Supply chain management runs on millions of daily decisions. Most are routine, many are reactive, but few drive real advantage. Agentic AI changes that.

Traditional automation breaks when something — even a single unexpected variable — veers slightly from what’s expected. Agentic AI is much more flexible. It evaluates the situation, weighs what matters most at that moment, and adjusts accordingly.

For example, when a shipment is delayed, it evaluates alternate suppliers, weighs the cost and service impacts, adjusts schedules, and executes the best response before your team even sees the alert… unless you’ve set a rule to automatically notify you somewhere earlier in the process.

Agentic systems run on a sense–plan–act–learn loop. They read live data, analyze scenarios against business goals, act directly in connected systems, and use each outcome to refine and make future decisions. 

With each loop, the system improves. Issues that used to take hours to resolve are handled in minutes. Your team stops reacting to problems and starts focusing on strategy. And the system learns to navigate the trade-offs between cost, service, and risk better than any spreadsheet ever could.

Procurement teams can use agentic systems to automatically reconcile data, flag discrepancies, and uncover savings opportunities. Planning teams can run continuous “what-if” simulations and act on AI recommendations rather than relying on static reports that are subject to interpretation.

For supply chain leaders, agentic AI turns complexity into advantage. Start where your data is clean and your processes are repeatable, and you may quickly see measurable efficiency, resilience, and ROI.

How agentic AI improves resilience and ROI

Enterprises that deploy agentic AI are already seeing measurable impact, like a 43% increase in real-time spend visibility and over 30% improvements in procurement compliance ratings and inventory turnover. But the real advantage comes from what happens when thousands of everyday decisions get smarter at once.

Traditional supply chains react to disruptions after they happen. A supplier delay triggers alerts, teams scramble, and costs rise as service levels slip. 

Agentic systems flip that dynamic. They spot trouble brewing (like a supplier running behind or weather disrupting a major route) and immediately find alternatives. High-quality ones, at that. 

By the time that problem would have hit your inbox, agentic systems have already rerouted shipments, secured backup capacity, or adjusted production schedules. Now the volatility that keeps your competitors scrambling becomes your competitive advantage.

And saving money is just the beginning. When agents address issues before they happen, your planners stop reacting defensively and start thinking strategically. The whole operation runs more smoothly, with fewer emergency orders and risk mitigation baked into every decision.

That efficiency and foresight are what enables agentic AI to pay for itself. The trillion-dollar opportunity in supply chain AI isn’t from a single big project. It’s the thousands of daily intelligent, automated decisions that make your system a worthwhile long-term investment.

Key domains for AI agents in supply chains

Agentic AI delivers impact across the entire supply chain; four domains consistently show the highest return on investment, which can ultimately help prioritize implementation and build momentum for more use cases.

Inventory and demand forecasting

Forecasting and inventory decisions shouldn’t depend on static rules or quarterly reviews. Agentic AI turns these slow, manual processes into live, adaptive systems so you’re always aware of changes or new developments.

Agentic systems can monitor everything: sales patterns, inventory levels, seasonal patterns, weather, social trends, market shifts, and more. This allows them to forecast demand and act on decisions immediately, rebalancing stock and triggering replenishment orders before demand even hits. 

And because most organizations already have forecasting processes in place, this is often the fastest path to ROI. DataRobot’s agentic AI platform takes existing workflows even further by automating analysis, surfacing risks, and executing multiple planning scenarios, leading to smarter decisions, faster responses, and measurable gains.

Dynamic sourcing and procurement

Procurement doesn’t have to wait for the next RFP cycle. Agentic AI turns sourcing into a continuous, always-on function that drives efficiency, savings, and resilience.

Agents constantly scan supplier markets, evaluate performance metrics, and manage routine negotiations independently (within defined parameters). They identify and qualify new vendors as conditions change, keeping backup options at the ready before disruptions hit.

Risk and cost management also become proactive. Agents track everything that could go wrong — like supplier bankruptcies, geopolitical tensions, and performance drops — and adjust your sourcing strategy before you’re caught unprepared. Pricing decisions change dynamically, too, with agents optimizing based on live market data, rather than last quarter’s terms.

Through this dynamic sourcing, costs drop, supply security improves, and teams spend less time fixing issues and more time driving strategic value.

Logistics and transportation

Transportation and logistics generate massive amounts of real-time data: GPS tracking, traffic conditions, weather forecasts, and carrier capacity. 

• Route optimization becomes dynamic, with agents adjusting delivery paths based on traffic, weather, and changing priorities throughout the day. 
• Carrier management goes from manual booking to automatic selection based on cost, reliability, and capacity. Exception handling also becomes proactive. 
• Agents can reroute shipments when they detect potential delays, rather than waiting for problems to materialize.

The integration with IoT sensors and GPS tracking creates a feedback loop that continuously improves decision-making. Agents learn which carriers perform best under specific conditions, which routes are most reliable at different times, and how to balance speed versus cost across changing priorities.

Predictive maintenance and shop floor optimization

Your equipment is talking, but many operations aren’t listening. Agentic AI turns machine data into action, predicting failures, scheduling maintenance, and optimizing production plans.

So instead of time-based maintenance, agents use live sensor data to detect early warning signs and schedule service when it’s needed, minimizing downtime and extending asset life. On the shop floor, agents rebalance production based on equipment availability, demand priorities, and resource constraints, eliminating manual planning cycles that quickly become outdated.

The impact compounds quickly due to fewer breakdowns, higher throughput, better resource utilization, and tighter scheduling. It’s more output from the same assets, but without additional cost.

Technology foundations for agentic AI in supply chains

Beyond smart algorithms, building effective agentic applications takes a connected, reliable, and scalable technology foundation. Supply chains run on complexity, and agentic AI depends on data flow, interoperability, and (perhaps most importantly) governance to make autonomous decisions you can trust.

The technology stack that allows for this is built in multiple connected layers:

• Data fabric: Provides unified access to ERP, WMS, TMS, and external data sources. This is your real-time data flow that agents can use for consistent, accurate inputs. Without clean, accessible data, even the smartest agents will make poor decisions.
• AI/ML platform: Models are built, trained, and deployed here, then continuously updated as markets shift. Whether agents need to forecast demand, optimize routes, or simulate scenarios, the AI and machine learning platform keeps them sharp and adaptable.
• Agent orchestration: In connected systems, agents stay aligned and working together, not against one another. Your procurement agent won’t buy inventory when your logistics agent doesn’t have warehouse space. 
• Integration middleware: This layer is the bridge between thinking and doing, letting agents place orders, shift schedules, and update systems directly through APIs. 
• Monitoring and governance: Every decision is tracked, enforcing compliance rules and maintaining audit trails. Governance is about building trust through accountability and ongoing improvement.

The hardest part isn’t building the agents. It’s connecting them. Supply chain data lives everywhere, from filesystems and databases to APIs, each with its own standards and constraints. And joining and standardizing that data is (historically) slow, error-prone, and costly.

DataRobot’s enterprise AI platform delivers a solution in an integrated architecture, allowing teams to build, deploy, and manage agentic systems at scale while maintaining security and oversight. It handles the technical complexity, so leaders can zero in on results instead of wrestling with how everything fits together.

Building an autonomous flow

Implementing agentic AI doesn’t mean replacing your entire supply chain overnight. You systematically identify high-impact opportunities and build autonomous capabilities that evolve over time. Here’s the roadmap for getting it right.

Step 1: Define objectives and use cases

The first step is knowing where agentic AI will quickly deliver measurable impact. Start with decision-heavy workflows that occur frequently, draw from multiple data sources, and directly affect cost, service, or efficiency.

Ideal early use cases include purchase order approvals, inventory reorder decisions, or shipment routing. These processes have well-defined success metrics, but too many variables for effective manual decision-making.

This is where agentic automation builds momentum and trust. Start with operational use cases, prove value quickly, and scale from there. The credibility for this system will grow as the AI agent delivers tangible efficiency and cost gains.

Step 2: Integrate real-time data

Agentic AI is only as effective as the data it runs on. Without a real-time feed from every critical source (ERP, inventory systems, IoT sensors, market feeds, supplier portals), agents are siloed and forced to guess. They need the full picture, updated constantly, to make decisions you can trust.

This integration provides access to trustworthy, consistent data flowing at the speed of your operations. Clean, standardized, and validated inputs prevent bad data from driving bad decisions.

Step 3: Develop and train AI agents

Once the data is connected, the next step is to build agents that understand your business and act with intent. Training combines historical data, business rules, and performance metrics so agents learn what successful decisions look like and how to repeat them at scale.

Agents need to learn from both data patterns and human expertise on supply chain trade-offs (cost, service level, and risk). This creates agents that can make context-aware decisions automatically, turning knowledge into repeatable, scalable efficiency.

Step 4: Pilot in a sandbox environment

It’s important to test everything in a sandbox environment first, using real-world scenarios (supplier failures, demand spikes, weather disruptions) to see how it performs. Compare their decisions to what your team would do in the same situation. Then fix what’s broken before going live.

The pilot phase shows the system works and builds trust with your teams. When they see agents successfully handling scenarios, skepticism turns to support. And that success will help to sell the next phase of automation.

Step 5: Scale with governance and monitoring

Once agents prove their value, scale deliberately and transparently. Start with lower-risk decisions while maintaining human oversight. Watch its performance so you can fine-tune models as conditions change.

Monitoring performance also applies to avoiding the hidden costs of agentic AI. You want to be mindful during this phase to prevent surprises and maintain trust. Again, the objective isn’t complete automation overnight. You want to scale what works, but do so with intention and awareness.

Common challenges with agentic AI supply chains and how to mitigate them

The best agentic AI strategy can still stall without the right foundations. The three most common challenges — fragmented data, operator resistance, and compliance complexity — can make or break adoption.

1. Disconnected data
When your systems don’t talk to each other, agents work with incomplete information and make poor decisions as a result. The solution starts with real-time data quality monitoring and standardized data models across all of your connected systems. 

Putting validation rules directly into agent logic ensures decisions are based on accurate, consistent information. And clean, reliable data turns automation from risky to repeatable.

2. Team resistance
Supply chain professionals are (rightfully) cautious about handing decisions to machines. Build trust by keeping people in the loop for critical decisions, starting with low-risk, high-visibility workflows and maintaining transparent audit trails that explain every recommendation (and how it ended up there). 

3. Compliance concerns
Supply chain lives and dies by its regulations, contracts, and audits. And that won’t change even with AI entering the picture. It will, however, build compliance into your agents’ DNA from Day 1, teaching them your regulatory requirements as core decision criteria. 

Every action requires a paper trail that auditors can follow, and human teams need the ability to step in when necessary. When governance is part of the architecture rather than patched on later, you can scale with confidence.

While these might be challenges, they aren’t barriers. When data quality, trust, and governance are built into your agentic architecture from the start, the benefits easily scale with you as you grow.

Scaling smart supply chains with DataRobot

The leap from proof of concept to production-ready agentic AI starts with a solid foundation. Transforming the supply chain lifecycle through agentic AI takes a platform built for real-world complexity, scale, and accountability. 

DataRobot delivers the enterprise-grade infrastructure that supply chain operations need to scale automation safely and efficiently with secure architecture, pre-built accelerators, built-in platform governance, and integration with your existing ERP, WMS, and TMS systems.

Your supply chain is already making thousands of decisions a day. But are those decisions getting smarter? Agentic AI answers that question with a resounding, “Yes!” turning your automation into intelligence.

Learn why supply chain leaders are choosing DataRobot to maximize AI impact and confidently move from reactive to intelligent.

FAQs

How is agentic AI different from traditional supply chain automation?
Traditional automation follows predefined rules and breaks when variables shift. Agentic AI uses a continuous loop of sensing, planning, acting, and learning—allowing it to adapt to real-world conditions and make autonomous decisions in real time.

Where should companies start with agentic AI in the supply chain?
Begin with high-volume, decision-heavy processes where the data is already clean and structured—like demand forecasting, shipment routing, or PO approvals. These areas allow teams to see ROI quickly and build internal trust in the system.

What kind of ROI can companies expect?
The ROI of agentic AI compounds over time as thousands of routine decisions become faster and smarter. Companies often see improved inventory turnover, fewer disruptions, reduced manual effort, and stronger supplier performance—driving both savings and service improvements.

Does agentic AI require replacing existing supply chain systems?
No. Agentic AI is designed to layer onto your current ERP, WMS, and TMS systems through APIs and middleware. The goal is to orchestrate decisions across systems, not replace them entirely.

The post Using agentic applications to build a smarter supply chain appeared first on DataRobot.

Two robots the size of schoolchildren stepped into the ring at BattleBots Arena.

While the capabilities of robots have improved significantly over the past decades, they are not always able to reliably and safely move in unknown, dynamic and complex environments. To move in their surroundings, robots rely on algorithms that process data collected by sensors or cameras and plan future actions accordingly.

The required solution would need to provide seamless connectivity and communication with the RoboDrill machines, as well as additional factory machinery throughout the company targeted for robotic automation moving forward.

Humanoid robots danced, somersaulted, dealt blackjack and played ping-pong at the Consumer Electronics Show this week, but some in the industry are impatient for them to become more useful, not just a promise of things to come.

Researchers have built a new platform that produces ultrashort UV-C laser pulses and detects them at room temperature using atom-thin materials. The light flashes last just femtoseconds and can be used to send encoded messages through open space. The system relies on efficient laser generation and highly responsive sensors that scale well for manufacturing. Together, these advances could accelerate the development of next-generation photonic technologies.

A collaboration between Princeton University engineers and entomologists at the University of Illinois Urbana-Champaign began with the researchers chasing grasshoppers in a hot parking lot. Their eventual focus on the hindwings of one species of grasshopper, Schistocerca americana, the American grasshopper, is inspiring a new approach to untethered gliding flight.

With their application, the team at Robotecki not only reduced labor-dependency for these key tasks, they improved the quality of production and throughput.

Prototype robotic dogs built by Texas A&M University engineering students and powered by artificial intelligence demonstrate their advanced navigation capabilities. Photo credit: Logan Jinks/Texas A&M University College of Engineering.

By Jennifer Nichols

Meet the robotic dog with a memory like an elephant and the instincts of a seasoned first responder.

Developed by Texas A&M University engineering students, this AI-powered robotic dog doesn’t just follow commands. Designed to navigate chaos with precision, the robot could help revolutionize search-and-rescue missions, disaster response and many other emergency operations.

Sandun Vitharana, an engineering technology master’s student, and Sanjaya Mallikarachchi, an interdisciplinary engineering doctoral student, spearheaded the invention of the robotic dog. It can process voice commands and uses AI and camera input to perform path planning and identify objects.

A roboticist would describe it as a terrestrial robot that uses a memory-driven navigation system powered by a multimodal large language model (MLLM). This system interprets visual inputs and generates routing decisions, integrating environmental image capture, high-level reasoning, and path optimization, combined with a hybrid control architecture that enables both strategic planning and real-time adjustments.

A pair of robotic dogs with the ability to navigate through artificial intelligence climb concrete obstacles during a demonstration of their capabilities. Photo credit: Logan Jinks/Texas A&M University College of Engineering.

Robot navigation has evolved from simple landmark-based methods to complex computational systems integrating various sensory sources. However, navigating in unpredictable and unstructured environments like disaster zones or remote areas has remained difficult in autonomous exploration, where efficiency and adaptability are critical.

While robot dogs and large language model-based navigation exist in different contexts, it is a unique concept to combine a custom MLLM with a visual memory-based system, especially in a general-purpose and modular framework.

“Some academic and commercial systems have integrated language or vision models into robotics,” said Vitharana. “However, we haven’t seen an approach that leverages MLLM-based memory navigation in the structured way we describe, especially with custom pseudocode guiding decision logic.”

Mallikarachchi and Vitharana began by exploring how an MLLM could interpret visual data from a camera in a robotic system. With support from the National Science Foundation, they combined this idea with voice commands to build a natural and intuitive system to show how vision, memory and language can come together interactively. The robot can quickly respond to avoid a collision and handles high-level planning by using the custom MLLM to analyze its current view and plan how best to proceed.

“Moving forward, this kind of control structure will likely become a common standard for human-like robots,” Mallikarachchi explained.

The robot’s memory-based system allows it to recall and reuse previously traveled paths, making navigation more efficient by reducing repeated exploration. This ability is critical in search-and-rescue missions, especially in unmapped areas and GPS-denied environments.

The potential applications could extend well beyond emergency response. Hospitals, warehouses and other large facilities could use the robots to improve efficiency. Its advanced navigation system might also assist people with visual impairments, explore minefields or perform reconnaissance in hazardous areas.

Nuralem Abizov, Amanzhol Bektemessov and Aidos Ibrayev from Kazakhstan’s International Engineering and Technological University developed the ROS2 infrastructure for the project. HG Chamika Wijayagrahi from the UK’s Coventry University supported the map design and the analysis of experimental results.

Vitharana and Mallikarachchi presented the robot and demonstrated its capabilities at the recent 22nd International Conference on Ubiquitous Robots. The research was published in A Walk to Remember: MLLM Memory-Driven Visual Navigation.

The real driver of change has been the need to reduce manual intervention and lower operational costs. When a robot can charge itself safely and autonomously, maintenance downtime drops dramatically, and so does total cost of ownership.

Researchers have created microscopic robots so small they’re barely visible, yet smart enough to sense, decide, and move completely on their own. Powered by light and equipped with tiny computers, the robots swim by manipulating electric fields rather than using moving parts. They can detect temperature changes, follow programmed paths, and even work together in groups. The breakthrough marks the first truly autonomous robots at this microscopic scale.

Hyundai-owned Boston Dynamics publicly demonstrated its humanoid robot Atlas for the first time Monday at the CES tech showcase, ratcheting up a competition with Tesla and other rivals to build robots that look like people and do things that people do.

This enables, for instance, intelligent robots and drones that can operate independently in a rescue mission after an earthquake without constant network connections or bulky batteries.

In future years, AI writing in 2025 will most often be remembered as the year Google grew tired of being an also-ran and decisively grabbed the crown as the ‘Titan to Beat’ when it comes to AI automated thinking, writing and imaging.

That bold move by Google has been a great boon to writers, who can look forward to ever-more-fierce competition among AI’s key players in coming years – and ever more sophisticated AI writing tools.

Meanwhile, 2025 also decisively etched in the minds of business leaders that AI was more than simply a stunning wonder: It also became one the world’s most formidable new competitive tools that tech has to offer.

Specifically: Studies emerged that general use of ChatGPT at businesses was resulting in major productivity gains.

And still other studies found that ChatGPT and similar AI were logging significant productivity and quality of writing gains when AI was specifically used to auto-generate emails at businesses.

Meanwhile, AI grew significantly more intelligent, with ChatGPT releasing an AI engine deemed smarter than 98% of all humans.

Plus, a darkhorse research team from China shocked the world by releasing an AI engine nearly as good as ChatGPT that was built for pennies-on-the-dollar.

Bottom line: Given all the breakneck advances in AI during 2025, even the most skeptical can no longer claim that AI is a fanciful creation of the AI hype machine.

Instead, even the most skeptical must come to realize AI is the real deal.

And even the most skeptical must come to agree that AI and all its permutations will change the world as we know it.

Here’s detail on the top stories of the year that helped shape that takeaway:

*Gemini 3.0: The New Gold Standard In AI: After years of watching glumly from the sidelines as a nimble new start-up – ChatGPT – ate its lunch and soared to record-breaking, worldwide popularity, Google has finally decried “enough is enough” and released a new chatbot that’s literally in a league of its own.

Dubbed Gemini 3.0, the new AI definitively dusts its nearest overall competitor – ChatGPT-5.1 – across a wide array of critical, benchmark tests.

(A few weeks after this story broke, ChatGPT 5.2 was released, significantly reducing Gemini 3.0’s new lead in AI.)

*ChatGPT’s Top Use at Work: Writing: A new study by ChatGPT’s maker finds that writing is the number one use for the tool at work.

Observes the study’s lead researcher Aaron Chatterji: “Work usage is more common from educated users in highly paid professional occupations.”

Another major study finding: Once mostly embraced by men, ChatGPT is now popular with women.

Specifically, researchers found that by July 2025, 52% of ChatGPT users had names that could be classified as feminine.

*Bringing in ChatGPT for Email: The Business Case: While AI coders push the tech to ever-loftier heights, one thing we already know for sure is AI can write emails at the world-class level — in a flash.

True, long-term, AI may one day trigger a world in which AI-powered machines do all the work as we navigate a world resplendent with abundance.

But in the here and now, AI is already saving businesses and organizations serious coin in terms of slashing time spent on email, synthesizing ideas in new ways, ending email drudgery as we know it and boosting staff morale.

Essentially: There are all sorts of reasons for businesses and organizations to bring-in bleeding edge AI tools like ChatGPT, Gemini, Anthropic, Claude and similar to take over the heavy lifting when it comes to email.

This piece offers up the Top Ten.

*AI Users: ‘AI Has Tripled My Productivity:’ A new survey of U.S. workers finds they’re reducing the time it takes to complete some tasks by as much as two-thirds.

Moreover, 40% of U.S. workers reported they were using AI in some way in April 2025 –- as compared to 30% of workers just four months prior.

Even so, more gains would be possible if more of these early adopters would leverage relatively sophisticated applications of AI, such as AI-powered, deep research, AI agents and similar advanced AI systems, according to Ethan Mollick, a business technology professor at the University of Pennsylvania.

*New ChatGPT AI Engine Smarter than 98% of Humans: Stick a fork in it: Apparently, the battle of wits between humans and AI is so yesterday — and we flesh-bags have lost.

New test results from Mensa — the global group of the rumoredly smartest people in the world — show that one of ChatGPT’s newest AI engines, o3, has an IQ of 136.

Observes writer Liam Wright: “The score, calculated from a seven-run rolling average, places the model above approximately 98% of the human population.”

Currently, ChatGPT runs on a number of specialized AI engines — including ChatGPT-4o, which is rated best overall for writing.

ChatGPT-o3 was designed to excel in reasoning, math and other hard sciences applications.

*’Tweaked’ AI Writing Can Now Be Copyrighted: In a far-reaching decision, the U.S. Copyright Office has ruled that AI-generated content — modified by humans — can now be copyrighted.

The move has incredibly positive ramifications for writers who polish output from ChatGPT and similar AI to create blog posts, articles, books, poetry and more.

Observes writer Jacqueline So: “The U.S. Copyright Office processes approximately 500,000 copyright applications each year, with an increasing number being requests to copyright AI-generated works.”

“Most copyright decisions are made on a case-to-case basis.”

*ChatGPT-Maker Brings Back ChatGPT-4o, Other Legacy AI Engines: Responding to significant consumer backlash, OpenAI has restored access to GPT-4 and other legacy models that were popular before the release of GPT-5.

Essentially, many users were turned-off by GPT-5’s initial personality, which was perceived as cold, distant and terse.

Observes writer Will Knight: “The backlash has sparked a fresh debate over the psychological attachments some users form with chatbots trained to push their emotional buttons.”

*How DeepSeek Outsmarted the Market and Built a Highly Competitive AI Writer/Chatbot: New York Times writer Cade Metz offers an insightful look in this piece into how newcomer DeepSeek built its AI for pennies-on-the-dollar.

The chatbot stunned AI researchers — and roiled the stock market in February — after showing the world it could develop advanced AI for six million dollars.

DeepSeek’s secret: Moxie. Facing severely restricted access to the bleeding-edge chips needed to develop advanced AI, DeepSeek made-up for that deficiency by writing code that was much smarter and much more efficient than that of many competitors.

The bonus for consumers: “Because the Chinese start-up has shared its methods with other AI researchers, its technological tricks are poised to significantly reduce the cost of building AI.”

*Use AI or You’re Fired: In another sign that the days of ‘AI is Your Buddy’ are fading fast, increasing numbers of businesses have turned to strong-arming employees when it comes to AI.

Observes Wall Street Journal writer Lindsay Ellis: “Rank-and-file employees across corporate America have grown worried over the past few years about being replaced by AI.

“Something else is happening now: AI is costing workers their jobs if their bosses believe they aren’t embracing the technology fast enough.”

*Solution to AI Bubble Fears: U.S. Government?: The Wall Street Journal reports that AI is now considered so essential to U.S. defense, the U.S. government may step in to save the AI industry — should it implode from the irrational exuberance of investors.

Observes lead writer Sarah Myers West: “The federal government is already bailing out the AI industry with regulatory changes and public funds that will protect companies in the event of a private sector pullback.

“Despite the lukewarm market signals, the U.S. government seems intent on backstopping American AI — no matter what.”

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Top Ten Stories of the Year in AI Writing: 2025 appeared first on Robot Writers AI.

New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers redesigned AI systems to better resemble biological brains, some models produced brain-like activity without any training at all. This challenges today’s data-hungry approach to AI development. The work suggests smarter design could dramatically speed up learning while slashing costs and energy use.