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While the battle of the AI titans raged on in Q2, 2025, ChatGPT – in many respects – ended up ‘owning’ the quarter.

New figures from Similarweb, for example, revealed that ChatGPT currently controls 80% of the AI market – and attracts more AI users than its next nine competitors combined.

Meanwhile, ChatGPT also offered a flurry of upgrades during the quarter, including:

–The ability to connect ChatGPT directly to your data library via OneDrive, SharePoint and other popular database platforms

–A memory boost that enables ChatGPT to remember every one of your interactions — so it can grow continually more adept at serving your AI needs

–A jump in overall smarts, which officially made ChatGPT smarter than 98% of all humans

Even so, a Gallup poll found that despite all the powerhouse AI currently available from ChatGPT and its competitors, only 8% of human workers use AI on a daily basis.

Plus, inexperienced users of AI continued to end-up with egg-on-their-face, including the Chicago Sun-Times, which published a ‘summer fun’ guide riddled with AI-hallucinated facts.

Here’s a complete rundown of the Top Ten Stories that helped shape AI in Q2, 2025:

*ChatGPT Controls 80% of the AI Market: Despite their best efforts, competitors to ChatGPT are unable to approach the chatbot’s influence worldwide.

Observes writer Jose Antonio Lanz: “ChatGPT attracts more traffic than the next nine AI tools combined, with 5.5 billion visits crushing Gemini and Claude.

“ChatGPT has become the default AI assistant for hundreds of millions of users worldwide.”

*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.

*ChatGPT Now Connects to Your Data Library on OneDrive or SharePoint: Writers and researchers with a wealth of data stored on MS OneDrive or SharePoint have a new, competitive advantage: They can now seamlessly integrate those databases with ChatGPT Deep Research.

The new feature, still in beta testing, enables users to prompt ChatGPT’s Deep Research tool to search those databases – which is especially handy if you know that the data you’re looking for is there, but you don’t know precisely where.

Other database platforms that also integrate with ChatGPT – at least in this beta application – are Dropbox and GitHub.

ChatGPT Plus, Pro and Team subscribers already have access to this extremely powerful new capability.

Access for ChatGPT Education and Enterprise subscribers is promised soon.

*ChatGPT: Matching the Right AI Engine for Your Task: ChatGPT runs on a number of different AI engines these days – each optimized for specific tasks.

Here’s the breakdown:

–Everyday writing: ChatGPT-4o is the go-to alternative for everyday writing tasks. It’s heavily tried, true and tested.

–Advanced Creative Writing: ChatGPT-4.5 is billed as an advanced creative writing tool – especially for users looking for AI with advanced emotional intelligence. The only downside: If you’re on ChatGPT Plus, you can only send 20 messages to ChatGPT-4.5 each month.

*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.

*ChatGPT Gets a Memory Boost: ChatGPT’s memory — which helps many writers by getting to know how and why they’re using ChatGPT — just got a boost.

Writer Cecily Mauran reports that “ChatGPT can now reference all of your past chats to provide more personalized responses.

“In addition to the saved memories that were there before, it can now reference your past chats to deliver responses that feel noticeably more relevant and useful.”

*Gallup: Only 8% of U.S. Workers Use AI Daily: In a stunning finding, a Gallup poll reveals that only 8% of U.S. workers use AI on a daily basis.

This in the face of other, markedly different study findings that indicate interest in AI is skyrocketing — including a DemandSage report finding that ChatGPT alone enjoyed 4.5 billion visits in March 2025.

Even more perplexing: Widespread AI adoption at the workplace is currently most prevalent in jobs that are heavily dependent on creativity and in-depth analysis, such as marketing, financial and similar reporting, law and IT.

That could mean that after accounting for the heavy use of AI in all those creativity/analysis heavy jobs, as little as 2% of rank-and-file white collar workers are actually using AI on the job every day.

The bottom line: Apparently, based on this June 2025 poll, U.S. business still has not picked-up on the message that using AI for something as simple as email — in terms of cost savings alone — is a no-brainer.

*Washington Post: Got an Opinion? Let Our AI Write It for You: Sages on bar stools across the globe, rejoice: The Washington Post is working on new AI that will forge your words of wisdom into op-ed gems.

Dubbed ‘Ember,’ the tool promises to automate several functions provided by human editors, including helping amateur writers develop an early thesis, supporting points and a memorable ending to their unique perspectives.

Observes writer Emma Roth: “The move is reportedly part of a broader initiative to open the paper to outside opinion pieces.”

*Now Made by AI: 30,000 Hyper-Local Newsletters: Thousands of town and neighborhood newsletters once curated by human beings are now being processed by AI, according to newsletter giant Patch.

Essentially, Patch discovered the hyper-local newsletters simply could not be sustained using human curation.

Observes Simone Wilson, former product manager, Patch, explaining the need for the switch: “There were certain communities where we couldn’t sell an ad to save our lives.”

*Oops: Chicago Sun-Times Publishes AI-Generated Gibberish: In yet another egg-on-my-face AI moment, a Chicago newspaper published an AI guide to summer fun that features made-up books and experts.

According to writer Mia Sato, the AI-generated, hallucinatory guide was created by Hearst Media and then published by the Chicago Sun-Times without so much as a quick glance to verify accuracy.

‘Facts-take-a-holiday’ moments in the guide include the non-existent book, “Nightshade Market,” the nonexistent food expert, Dr. Catherine Frost and the non-existent professor of leisure studies, Dr. Jennifer Campos.

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 in AI Writing, Q2 2025 appeared first on Robot Writers AI.

Dassault Systèmes’ 3DEXPERIENCE platform helped Raphe mPhibr reduce prototype development time from years to three months. The 3DEXPERIENCE platform integrates design, simulation and collaboration, supporting in-house design and manufacturing

Oblivious to the punishing midday heat, a wheeled robot powered by the sun and infused with artificial intelligence carefully combs a cotton field in California, plucking out weeds.

Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties.

The more we interact with robots, the more human we perceive them to become—according to new research from the University of East Anglia, published in the Journal of Experimental Psychology: Human Perception and Performance.

An advanced Johns Hopkins AI model called MAARS combs through underused heart MRI scans and complete medical records to spot hidden scar patterns that signal sudden cardiac death, dramatically outperforming current dice-roll clinical guidelines and promising to save lives while sparing patients unnecessary defibrillators.

A multinational team has cracked a long-standing barrier to reliable quantum computing by inventing an algorithm that lets ordinary computers faithfully mimic a fault-tolerant quantum circuit built on the notoriously tricky GKP bosonic code, promising a crucial test-bed for future quantum hardware.

A research group at the University of Klagenfurt has designed a real-time capable drone based on object-relative navigation using artificial intelligence. Also on board: a USB3 Vision industrial camera from the uEye LE family from IDS Imaging Development Systems GmbH.

The future of moon exploration may be rolling around a nondescript office on the CU Boulder campus. Here, a robot about as wide as a large pizza scoots forward on three wheels. It uses an arm with a claw at one end to pick up a plastic block from the floor, then set it back down.

Building production-grade agentic AI applications isn’t just about assembling components. It takes deep expertise to design workflows that align business needs with technical complexity. 

AI teams must evaluate countless configurations, balancing LLMs, smaller models, embedding strategies, and guardrails, while meeting strict quality, latency and cost objectives.

But developing agentic AI applications is only half the battle. 

AI teams often face challenges handing projects off to DevOps or MLOps teams to stand up the experience, integrating them into existing tools and workflows, and managing monitoring, governance, and complex GPU infrastructure at scale.

Without the right structure, agentic AI risks getting stuck in endless iterations. 

But when done right, agentic AI becomes more than just another application. It’s a transformative force empowering teams to build scalable, intelligent solutions that drive innovation, efficiency, and unprecedented business value. 

To make that leap, AI teams need more than just AI tools. They need a structured, scalable way to develop, deploy, and manage agentic AI efficiently. 

A complete AI stack for agentic AI development

Agentic AI can transform enterprise workflows, but most teams struggle to move from prototype to production. The challenge isn’t just building an agent — it’s scaling infrastructure reliably, delivering real value, and maintaining trust in the outputs as usage grows. 

To succeed, AI teams need more than disconnected tools. They need a simple, unified, end-to-end approach to development, deployment, and management. 

How DataRobot, accelerated by NVIDIA delivers agentic AI

Together, DataRobot and NVIDIA provide a pre-optimized AI stack, advanced orchestration tools, and a robust development and deployment environment, helping teams move faster from prototype to production while maintaining security and enterprise readiness from day one.

Here’s what this looks like.

The DataRobot agentic AI platform provides an end-to-end platform to orchestrate and manage the entire agentic AI lifecycle, enabling developers to build, deploy, and govern AI applications in days instead of months. 

With DataRobot, users can:

• Jumpstart development with customizable agentic AI app templates that offer pre-built workflows tailored to common, high-impact business problems.
  
• Streamline deployment of agentic AI apps on managed infrastructure using built-in guardrails and native integrations with enterprise tools and functions.
  
• Ensure enterprise-grade governance and observability with centralized asset tracking, built-in monitoring, and automated compliance reporting across any environment.
  
  

With NVIDIA AI Enterprise fully embedded into DataRobot, organizations can:

• Use performance-optimized AI model containers and enterprise grade-grade development software.
• Simplify deployment setup with NVIDIA NIM and NeMo microservices, that work out-of-the-box.
• Rapidly pull deployed NIM models into the playground and leverage DataRobot to build agentic AI apps without messing with configuration.
• Collaborate across AI and DevOps teams to deploy agentic AI applications quickly.
• Monitor and automatically improve all deployed agentic AI apps across environments.

10 steps to take agentic AI from prototype to production

Follow this step-by-step process for using DataRobot and NVIDIA AI Enterprise to build, operate, and govern your agentic AI quickly and efficiently. 

Step 1: Browse NVIDIA NIM gallery and register in DataRobot 

Access a full library of NVIDIA NIM directly within the DataRobot Registry. These pre-tuned, pre-configured components are optimized for NVIDIA GPUs, giving you a high-performance foundation without manual setup.

When imported, DataRobot automatically applies versioning and tagging, so you can skip setup steps and get straight to building.

To get started:

1. Open the NVIDIA NIM gallery within DataRobot’s registry.
2. Select and import the model into your registry.
3. Let DataRobot handle the setup. It will recommend the best hardware configuration, allowing you to focus on testing and optimizing instead of troubleshooting infrastructure.

Step 2: Select a DataRobot app template

Start compiling and configuring your agentic AI app with pre-built, customizable templates that eliminate setup work and let you go straight into prototyping, testing, and validating.

The DataRobot app library provides frameworks designed for real-world deployment, helping you get up and running quickly. 

1. Select a template that best matches your use case.
2. Open a codespace, which comes pre-configured with setup instructions.
3. Customize your app to run on NVIDIA NIM and fine-tune it for your needs

Step 3: Open your NVIDIA NIM into DataRobot Workbench to build and optimize your VDB

With your app template in place and hardware selected, it’s time to bring in the generative AI component and start building your vector database (VDB) in the DataRobot Workbench.

1. Open your NVIDIA NIM in the DataRobot Workbench. A use case will be created automatically.
2. Connect your data and navigate to the Vector Databases tab.
3. Select data sources and choose from multiple embedding models. DataRobot will automatically recommend one and provide alternatives to test.
   
   You can also import embedding and reranking models from NVIDIA in DataRobot Registry and make them available with the VDB creation interface.
4. Build one or multiple VDBs to compare performance before integrating them into your RAG workflow in the next step. 

Step 4: Test and evaluate NVIDIA NIM LLM configurations in the LLM Playground

In DataRobot’s LLM Playground, you can quickly build, compare, and optimize different RAG workflows and LLM configurations without tedious manual switching.

Here’s how to test and refine your setup:

1. Create a Playground within your existing use case.
2. Select LLMs, prompting strategies, and VDBs to include in your test.
3. Configure up to three workflows at a time and run queries to compare performance.
4. Analyze results and refine your configuration to optimize response accuracy and efficiency.

Step 5: Add predictive elements to your agentic flow

(If your app uses only generative AI, you can move on to packaging with guardrails and final testing.)

For agentic AI apps that incorporate forecasting or predictive tasks, DataRobot streamlines the process with its built-in predictive AI capabilities.

DataRobot will automatically:

• Analyze the data, detect feature types, and preprocess it.
• Train and evaluate multiple models, ranking them with the best-performing one at the top.

Then you can:

• Analyze key drivers behind the prediction.
• Compare different models to fine-tune accuracy.
• Integrate the selected model directly into your agentic AI app.

Step 6: Add the right tools to your app 

Expand your app’s capabilities by integrating additional tools and agents, such as the NVIDIA AI Blueprint for video search and summarization (VSS), to process video feeds and transform them into structured datasets.

Here’s how to enhance your app:

• Create additional tools or agents using frameworks like LangChain, NVIDIA AgentIQ, NeMo microservices, NVIDIA Blueprints, or options from the DataRobot library.
• Expand your data sources by integrating hyperscaler-grade tools that work across cloud, self-managed, and bare-metal environments.
• Deploy and test your app to ensure seamless integration with your generative and predictive AI components.

Step 7: Add monitoring and safety guardrails 

Guardrails are your first line of defense against bad outputs, security risks, and compliance issues. They help ensure AI-generated responses are accurate, secure, and aligned with user intent. 

Here’s how to add guardrails to your app:

1. Open your model in the Model Workshop.
2. Click “Configure” and navigate to the Guardrails section.
3. Select and apply built-in protections such as NVIDIA NeMo Guardrails, including:
   
   Stay on Topic
   Content Safety
   Jailbreak
4. Customize thresholds or add additional guardrails to align with your app’s specific requirements.

Step 8: Design and test your app’s UX

A well-designed UX makes your AI app intuitive, valuable, and easy to use. With DataRobot, you can stage a complete version of your app and test it with end users before deployment.

Here’s how to test and refine your UX:

• Stage your app in DataRobot for testing.
• Share it via link or embed it in a real-world environment to gather user feedback.
• Gain full visibility into how the app works, including chain of thought reasoning for transparency.
• Incorporate user feedback early to refine the experience and reduce costly rework.

Step 9: Deploy your agentic AI app with one-click

With one-click deployment, you can instantly launch NVIDIA NIMs from the model registry without manual setup, tuning, or infrastructure management. 

Your app, guardrails, and monitoring are deployed together, ensuring full traceability and governance.

Here’s how to deploy:

1. Select the NVIDIA NIM model you want to use.
2. Choose your GPU configuration and set any necessary runtime options—all from a single screen.
3. Deploy with one click. DataRobot automatically packages and registers your model with all necessary components.

Step 10: Monitor and govern your deployment in DataRobot

After deployment, your AI app requires continuous monitoring to ensure long-term stability, accuracy, and performance. NIM deployments use DataRobot’s observability framework to surface key metrics on health and usage.

The DataRobot Console provides a centralized view to:

• Track all AI applications in a single dashboard.
• Identify potential issues early before they impact performance.
• Drill down into individual prompts and deployments for deeper insights.

Avoid getting stuck in endless iteration

Complex AI projects often stall due to repetitive manual work — swapping components, tuning combinations, and re-running tests to meet evolving requirements. Without clear visibility or structured workflows, teams can easily lose track of what’s working and waste time redoing the same steps.

Best practices to reduce friction and maintain momentum:

• Test and compare as you go. Experiment with different configurations early to avoid unnecessary rework. DataRobot’s LLM Playground makes this fast and simple.
  
• Use structured workflows. Stay organized as you test variations in components and configurations.
  
• Leverage audit logs and governance tools. Maintain full visibility into changes, streamline collaboration, and reduce duplication. DataRobot can also generate compliance documentation as part of the process.
  
• Swap components seamlessly. Use a modular platform that lets you plug and play without disrupting your app.

By following these practices, you and your team can move faster, stay aligned, and avoid the iteration trap that slows down real progress.

Develop and deliver agentic AI that works

Agentic AI has massive potential, but its impact depends on delivering it efficiently and ensuring trust in production.

With DataRobot and NVIDIA AI Enterprise, teams gain:

• Pre-built templates to accelerate development
• Optimized NVIDIA NIM containers for high-performance execution
• Built-in guardrails and monitoring for safety and control
• A flexible, governed pipeline that adapts to enterprise needs

Whether you’re launching your first agentic AI app or scaling a portfolio of enterprise-grade solutions, this platform gives you the speed, structure, and reliability to turn innovation into real business results.

Ready to build? Book a demo with a DataRobot expert and see how fast you can go from prototype to production.

The post How to build and scale agentic AI with DataRobot and NVIDIA appeared first on DataRobot.

With so many NDT options available, the best one depends on the specific materials and functions in question. As a result, it is often most helpful to vary methods between components.

A new imaging technique developed by MIT researchers could enable quality-control robots in a warehouse to peer through a cardboard shipping box and see that the handle of a mug buried under packing peanuts is broken.

From delivering food in restaurants to cleaning airports, robots are becoming increasingly common in society—but are our policies ready to keep up?

From delivering food in restaurants to cleaning airports, robots are becoming increasingly common in society—but are our policies ready to keep up?

While so far robots have predominantly been deployed individually, as teams, they can tackle a wider range of complex missions with remarkable speed and efficiency. For instance, they could help to rapidly transport objects to target locations, moving on varying terrains and perhaps even passing through environments that are difficult for humans to access.