by Gary Mintchell | Mar 16, 2026 | Generative AI
AI news and opinion notes clog my sidebar of potential blogs. Go ahead, try getting an hour free from some AI hype or dire warning.
One I find a bit amusing is the fear of AI LLMs taking over writing.
I’m old enough to remember teachers telling students not to just copy from encyclopedia entries. Then there was copying from web pages.
I wrote a paper for my university freshman composition class (do they still have those?). It cited one major source book. I lived at home and used a book from my local library. Now, what are the odds that two students from a class of 40 would pick the same topic—Henrik Ibsen’s Concept of Truth in Peer Gynt? It happened.
The copy of that source disappeared from the university library. The instructor called me to her office. “Can you bring in the book?” she asked. No problem. Turns out the other student copied their paper from the book. Net result—I received an A and a suggestion that I major in English.
Cheating must be as old as schools.
Back to AI. I have recently guided Claude through a series of questions to research smart manufacturing. Wound up with many notes. I asked it to write an essay in the style of The Manufacturing Connection. It did.
I have now discovered what programming leaders are discovering about using AI to write code—checking the work and (in my case rewriting to suit me). That entails a lot of time and work.
Writing is thinking. If you want to think through something, don’t just copy AI. Write your thoughts and then organize them.
By the way, I’m getting press releases obviously written by AI. I can tell the grammar and phrasing.
Oh, I didn’t major in English. International politics and philosophy. Add that to all the math classes I took, it became an ideal background for working on the technical side of business.
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by Gary Mintchell | Mar 11, 2026 | Generative AI, Networking
I remember Digi International from a couple decades ago as a connectivity company. They went dark for many years, then has suddenly lit up my inbox since the pandemic. This news continues the connectivity path with something my brief acquaintance, Walker Reynolds, told me at the Ignition Community Conference—MCP is the next big thing. I heard from his conference that there was another “next big thing.” I think he was partially right—Model Context Protocol (MCP) for agents and Generative AI is a big thing. And Digi International has launched it as part of their connectivity solutions.
Digi International announced the launch of its new Model Context Protocol (MCP) server for Digi Remote Manager (DRM) and Genesis. This new capability enables customers to securely integrate large language model products such as Claude and other enterprise AI assistants directly with DRM and Genesis, transforming how organizations monitor, manage, and optimize their connected infrastructure and wireless WAN deployments (WWAN) at scale.
The MCP server allows DRM and Genesis users to leverage natural language interfaces to query device fleets, automate workflows, generate configuration insights, and streamline troubleshooting. By bridging enterprise AI tools with Digi’s secure device management platform, customers can accelerate operational efficiency, reduce complexity, and empower teams with intelligent, context aware insights across their connectivity deployments. The solution is designed with enterprise grade security and governance controls to ensure data protection and responsible AI integration.
This launch builds on a series of recent milestones for Digi. The company recently celebrated the successful deployment of a cellular router solution leveraging eSIM technology aligned with GSMA SGP.32 standards, reinforcing Digi’s leadership in next generation connectivity for distributed enterprises and remote SIM provisioning. Shortly thereafter, Digi became the first WWAN connectivity organization to achieve SOC 2 Type 2 compliance, underscoring its commitment to rigorous security, availability, and confidentiality standards for customers worldwide.
by Gary Mintchell | Mar 9, 2026 | Generative AI, Robots, Software
I walked into my local Starbucks this morning for my usual Doppio Espresso with cinnamon powder. I told my barista I was about to listen to a press conference on “physical AI.” “What do you think that is?” I asked her. “I don’t know. Maybe something like robots?” she countered. She saved me doing a deep dive with my buddy Claude.
The press conference was with ABB Robotics and NVIDIA announcing an expansion (for a fee) of ABB’s RobotStudio software to incorporate AI models establishing a new product called RobotStudio HyperReality coming to a computer near you in a few months.
- ABB Robotics integrates NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy
- New RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly and reliably manufacturers can scale production, reducing costs by up to 40% and accelerating time-to-market by 50%
- Full range and breadth of industrial applications, with real-world pilot being conducted by Foxconn in consumer electronics assembly
- At NVIDIA GTC, the robotic workforce company WORKR will showcase how it’s using the solution to help manufacturers across the U.S. address critical labor shortages
The collaboration focuses on combining ABB Robotics’ software programming, design and simulation suite, RobotStudio, with the physically accurate simulation power of NVIDIA Omniverse libraries to close technology’s long-standing ‘sim-to-real’ gap. Developers can simulate robots in digital twins and generate synthetic data to train their physical AI models, enabling businesses of all types and sizes to deploy AI-driven robotics for various industrial workflows.
Called RobotStudio HyperReality, the resulting physically accurate simulations and foundation models are endlessly optimized with real-world data feedback continuously improving the system. These models can be used to train any number of ABB robots, anywhere in the world, with the reliability and accuracy demanded by industry.
The long-standing deficit between simulation accuracy and real-world lighting, materials and environments is known as the ‘sim-to-real’ gap. For decades, this gap has limited the ability of manufacturers to design and develop advanced manufacturing processes in the virtual world.
By integrating NVIDIA Omniverse libraries into RobotStudio, ABB Robotics will deliver unprecedented robotics simulation and synthetic data generation capabilities that will allow intelligent robots to bridge this gap with up to 99 percent accuracy. ABB is the only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near perfect correlation between simulation and real world performance. Combined with ABB Robotics’ Absolute Accuracy technology, which reduces positioning errors from 8–15 mm to around 0.5 mm, ABB delivers unmatched precision in both virtual and physical environments, making it suited to high-precision industrial-grade applications.
ABB Robotics is also assessing the potential to integrate the NVIDIA Jetson edge computing plat-form into its Omnicore controller to achieve real-time AI inference at the edge for its extensive robot portfolio. Today’s announcement builds upon ABB Robotics’ long-standing work with NVIDIA, including the previous integration of NVIDIA Jetson into ABB Robotics’ VSLAM autonomous mobile robots as well as the development of gigawatt-scale AI data centers.
RobotStudio HyperReality will serve industrial clients at any scale, across a breadth of industries and applications, with select customers already testing its capabilities ahead of a full release to ABB Robotics’ 60,000 RobotStudio customers worldwide in the second half of 2026.
Foxconn, the world’s largest electronics contract manufacturer, is piloting the first joint use case in consumer electronics assembly. Automating the assembly of a tiny piece in consumer electronics is challenging, as multiple device variants require different production methods and the delicate metal structure requires precise pick-and-place and assembly control, as well as fine-tuned setup, often demanding additional debugging time and engineering resources. Using RobotStudio HyperReality, Foxconn’s assembly robots are trained virtually, using synthetic data to perfect multiple real-world production processes in various scenarios, before moving them to the production line with 99 percent accuracy. By optimizing production lines virtually, Foxconn will reduce set-up times and costs by eliminating physical training and tests, and accelerate time-to-market for consumer electronics.
WORKR, a California based robotic workforce company that delivers robotic manufacturing solu-tions to industry, is extending the reach of this technology to small and medium manufacturers across the United States. At NVIDIA GTC 2026 (March 16-19, San Jose, CA), WORKR will demonstrate AI- powered robotic systems built on ABB technology, trained with synthetic data using NVIDIA Omniverse libraries, and deployed without operators needing to know any program-ming. By combining ABB’s industrial grade robotics with its proprietary WorkrCore™ AI platform, the company is helping manufacturers address critical labor shortages with its robotic workforce that can learn new tasks in minutes and be operated by anyone.
by Gary Mintchell | Mar 3, 2026 | Generative AI, News
Deepgram continues to release news about extending its VoiceAI into new areas. Today’s news concerns collaboration with IBM. As with almost all new technologies, I see the potential uses but also see things I don’t care for. Voice AI (speech-to-text (STT) and text-to-speech (TTS)) has many uses, but I become annoyed when talking to customer service only to discover I’m talking with a computer with limited resources for help rather than a human. I guess that’s the future.
IBM and Deepgram announced a collaboration to integrate Deepgram’s speech-to-text (STT) and text-to-speech (TTS) capabilities into IBM’s watsonx Orchestrate generative AI solution.
To address client needs for highly performant, enterprise-grade transcription and real-time captioning, IBM will embed Deepgram’s capabilities into watsonx Orchestrate. This collaboration makes Deepgram IBM’s first voice partner, bringing voice AI technology that helps enterprises automate their operations and meet the growing demand for conversational AI technology, including advanced speech-to-text voice recognition so users can interact with digital agents using natural speech.
Many organizations are adopting AI-powered speech-to-text systems to automate transcription while handling real-world audio conditions, including background noise, diverse accents, and real-life dialog. This integration addresses these challenges by offering a wider range of languages and dialects, including dozens of Arabic and Indian variants, along with voices that reflect regional accents. It also adds options for custom tuning, real-time captioning and natural-sounding speech.
These technologies open new possibilities for enhanced automated customer care and support, call analysis, and voice-driven data entry in fields like healthcare and finance.
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by Gary Mintchell | Feb 27, 2026 | Business, Commentary, Generative AI
Update 2: Check out this Om Malik look at the Block news. He looks at how Dorsey tried to switch the narrative from bad management to AI. Wall Street rewarded job cuts–as it always does. Not vision.
Update: John Gruber at Daring Fireball takes a similar stance on the news (knowing the history of Jack Dorsey), while The New York Times takes the AI hype road in its headline regarding the news.
Media companies are struggling to get beyond the hype of AI into reality. Once my favorite news source, Axios has suddenly become the media cheerleader for AI. You can still get the gist of news there, but use your BS filter when approaching AI items. Morning Brew is usually much more balanced—and witty.
Case in point. I’m copying news about Jack Dorsey’s layoffs at Block. Remember Dorsey? He led Twitter before it had to sell. Seemingly everyone in Silicon Valley knew that Twitter was terribly bloated. Proof—Elon Musk slashed 80% of the workforce and the company continued to operate.
AI is, of course, another in a long line of automation tools that will assist humans in doing their jobs. Studies I’ve seen reveal some usefulness of AI in programming. But it’s far from actually replacing humans.
Dorsey goes to Block. Hmm. Seems like it was bloated, also. He announces a 40% reduction in workforce. He, like predecessor CEO such as at Amazon, blames AI for the ability of teams to do more with fewer people. Financial analysts typically look beyond AI into the basic financial need to reduce a bloated workforce.
I bet your experience mirrors my experience that smaller teams are more likely to get things done. If you’re not sure, I encourage checking out Jason Fried and David Heinemeier-Hansson of 37 Signals and The Rework Podcast.
As a professor at university used to use on tests—compare and contrast.
From Axios
1 big thing: Radical workforce cut may embolden CEOs
It was only a matter of time before a future-thinking CEO took the leap and replaced thousands of workers with AI, Axios’ Dan Primack writes.
Why it matters: Block chair Jack Dorsey did just that yesterday. Wall Street’s standing ovation — the fintech company’s shares soared more than 20% in today’s premarket trading — gives other CEOs permission, even incentive, to consider the same thing.
Dorsey, an iconoclast who co-founded and once led Twitter, was blunt in announcing via X that Block will say goodbye to 40% of its 10,000-person workforce, cutting the company to just under 6,000. Block, based in Oakland, Calif., includes Square, Cash App, Afterpay and the Tidal music platform.
Dorsey wrote on X that “something has changed. we’re already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company. and that’s accelerating rapidly.”
He said he “had two options: cut gradually over months or years as this shift plays out, or be honest about where we are and act on it now. i chose the latter.”
Dorsey added in a shareholder letter: “We’re already seeing it internally. A significantly smaller team, using the tools we’re building, can do more and do it better. And intelligence tool capabilities are compounding faster every week.”
Zoom in: The fintech’s stock rallied as much as 25% on the news, after having been down more than 16% over the past year and 76% over the past five years.
Block’s declining stock price put Dorsey under pressure to make changes, although he denied that the layoffs were related to Block’s financial performance.
Reality check: CEOs will look to see if they can follow Dorsey’s lead, and most will realize they don’t have the talent to do it. This would destroy most companies — it’s very hard to do.
The big picture: Wall Street was captivated early this week by a viral doomsday scenario for AI’s coming effect on white-collar work. But the stated reasons for most other big AI-related layoffs so far have been much less explicit than Dorsey’s.
Many AI executives and investors insist that the tech will lead to temporary labor dislocations rather than net job loss, echoing the Industrial Revolution.
The bottom line: It’s one thing to replace people with machines. It’s quite another to prove that it makes business sense. If Block can grow its top line with a much smaller headcount, the rest of corporate America will take notice.
From Morning Brew
Payments company Block to slash staff by 40% due to AI pivot. In conjunction with its earnings call yesterday, Block CEO Jack Dorsey said it will reduce headcount “from over 10,000 people to just under 6,000.” The company will restructure around “smaller, highly talented teams using AI to automate more work,” according to Block CFO Amrita Ahuja. Affected staff members will receive 20 weeks of base pay, which the company expects will contribute to $450 million to $500 million in charges, primarily in the first quarter. Dorsey also said, “Within the next year, I believe the majority of companies will reach the same conclusion and make similar structural changes.” Block stock rose 24% in after-hours trading following the announcement.—HVL
by Gary Mintchell | Feb 4, 2026 | Generative AI, News, Process Control, Security, Software
This news came last week. Just as I was contemplating the business model of cybersecurity firms following another acquisition, this news of a new company launch with a unique take on security. This company will be interesting to watch. The news comes from Amsterdam concerning the launch of a company called Indurex. Naturally they have AI in their product offering and manage to work in an older term—cyber-physical systems.
The quick take: An AI-powered, human-in-the-loop platform that brings together process safety and cybersecurity, turning complex signals into trusted decisions for resilient critical infrastructure.
Indurex, a pioneering artificial intelligence (AI) and cyber-physical systems (CPS) security company, announced on January 27 its official launch to help protect critical infrastructure, smart manufacturing, and connected industrial operations. The company’s mission is to deliver robust, adaptive security solutions that safeguard both the physical and digital worlds as they increasingly converge.
Founded by a team of seasoned experts in operational technology (OT), cybersecurity, and process safety systems, Indurex enters the market at a decisive time. Operators across energy, utilities, and manufacturing sectors face mounting challenges from IT-OT convergence, cyber sabotage, and cascading system failures — putting both process safety and cybersecurity integrity under increasing pressure and exposing essential assets to unprecedented risk. Traditional tools, designed for isolated IT networks or legacy control systems, can no longer assure the level of operational, safety, and cyber integrity required in today’s highly connected industrial environments.
Industrial organisations continue to face a critical gap between process safety and cybersecurity, which are managed in disconnected silos. Existing tools generate high volumes of alerts without sufficient industrial or engineering context, leading to alert fatigue and a limited ability to assess real operational and safety impact. At the same time, a new class of AI-enabled and cyber-physical threats is emerging — capable of exploiting process behaviour, safety dependencies, and human workflows. Detecting and stopping these threats requires AI-native technologies designed for industrial systems, combined with human-in-the-loop intelligence to ensure explainability, trust, and effective decision-making.
Indurex bridges this gap with an AI-native, interoperable platform that unifies engineering context and cybersecurity intelligence — an approach the company defines as Engineering Cyber Intelligence.
This delivers measurable returns across three dimensions:
- Operational Excellence & Safety Integrity: Fewer trips and faster recovery through unified situational awareness and continuous assurance of Safety Integrity Functions (SIF)
- Cyber Resilience: Contextualized detection and response across digital and physical domains, aligned with operational and safety impact
- Cost & Compliance: Automated reporting and defensible evidence of risk, control maturity, and safety integrity across critical systems
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