by Gary Mintchell | Apr 9, 2026 | Generative AI
Cal Newport, Computer Science PhD and Professor at Georgetown University, explains AI, LLMs, and the like better than anyone else I follow. His newsletter from last month, Why Hasn’t AI Made Work Easier?, explains some of the reports we’ve begun hearing through the media noise.
He writes:
I’ve been studying the intersection of digital technology and office work for quite some time. (I find it hard to believe that my book, Deep Work, just passed its ten-year anniversary!?) Here’s a pattern I’ve observed again and again:
(And, yes, I’ve lived through these…and more.)
- A new technology promises to speed up some annoying aspects of our jobs.
- Everyone gets excited about freeing up more time for deep work and leisure.
- We end up busier than before without producing more of the high-value output that actually moves the needle.
- This happened with the front-office IT revolution, and email, and mobile computing, and once again with video-conferencing.
Will AI be anything different?
I’m now starting to fear that we’re beginning to encounter the same thing with AI as well.
My worries were stoked, in part, by a recent article in the Wall Street Journal, titled “AI Isn’t Lightening Workloads. It’s Making Them More Intense.”
Based on some actual research:
The piece cites new research from the software company ActivTrak, which analyzed the digital activity of 164,000 workers across more than 1,000 employers. What makes the study notable is its methodology: it tracked individual AI users for 180 days before and after they began using these tools, providing clear insight into what changed. The results?
“ActivTrak found AI intensified activity across nearly every category: The time they spent on email, messaging and chat apps more than doubled, while their use of business-management tools, such as human-resources or accounting software, rose 94%.“
Ah, not everything was affected.
The one category where activity was not intensified, however, was deep work:
“[T]he amount of time AI users devoted to focused, uninterrupted work—the kind of concentration often required for figuring out complex problems, writing formulas, creating and strategizing—fell 9%, compared with nearly no change for nonusers.”
Why?
It’s not quite clear why AI tools are having this impact. One tantalizing clue, however, comes from Berkeley professor Aruna Ranganathan, who is quoted in the article saying: “AI makes additional tasks feel easy and accessible, creating a sense of momentum.”
I lived through these changes and concur:
This points toward a pattern similar to what happened when email first arrived. It was undeniably true that sending emails was more efficient than wrangling fax machines and voicemail. But once workers gained access to low-friction communication, they transformed their days into a furious flurry of back-and-forth messaging that felt “productive” in the abstract, activity-centric sense of that term, but ultimately hurt almost every other aspect of their jobs and made everyone miserable.
Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.
by Gary Mintchell | Apr 8, 2026 | Generative AI
I have cued up a couple of analyses on AI. Referring to Om Malik’s observation about generating news velocity, it’s hard to keep up. But the two I have remain relevant.
The first from an analyst I’ve followed for many years, Benedict Evans, End of Network Effect essay looks at the business model (or lack thereof) of OpenAI.
He opens:
OpenAI has some big questions. It doesn’t have unique tech. It has a big user base, but with limited engagement and stickiness and no network effect. The incumbents have matched the tech and are leveraging their product and distribution. And a lot of the value and leverage will come from new experiences that haven’t been invented yet, and it can’t invent all of those itself. What’s the plan?
He compares an OpenAI executive with Steve Jobs—where do you start when developing technology and a product?
“Jakub and Mark set the research direction for the long run. Then after months of work, something incredible emerges and I get a researcher pinging me saying: “I have something pretty cool. How are you going to use it in chat? How are you going to use it for our enterprise products?”
– Fidji Simo, head of Product at OpenAI, 2026
“You’ve got to start with the customer experience and work backwards to the technology. You can’t start with the technology and try to figure out where you’re going to try to sell it”
– Steve Jobs, 1997
Pretty damning.
Evans isolates four fundamental strategic questions.
Where’s the unique selling proposition?
First, the business as we see it today doesn’t have a strong, clear competitive lead. It doesn’t have a unique technology or product. The models have a very large user base, but very narrow engagement and stickiness, and no network effect or any other winner-takes-all effect so far that provides a clear path to turning that user base into something broader and durable. Nor does OpenAI have consumer products on top of the models themselves that have product-market fit.
It’s very early in the market cycle.
Second, the experience, product, value capture and strategic leverage in AI will all change an enormous amount in the next couple of years as the market develops. Big aggressive incumbents and thousands of entrepreneurs are trying to create new features, experiences and business models, and in the process try to turn foundation models themselves into commodity infrastructure sold at marginal cost. Having kicked off the LLM boom, OpenAI now has to invent a whole other set of new things as well, or at least fend off, co-opt and absorb the thousands of other people who are trying to do that.
They are all in the same boat.
Third, while much of this applies to everyone else in the field as well, OpenAI, like Anthropic, has to ‘cross the chasm’ across the ‘messy middle’ (insert your favourite startup book title here) without existing products that can act as distribution and make all of this a feature, and to compete in one of the most capital-intensive industries in history without cashflows from existing businesses to lean on. Of course, companies that do have all of that need to be able to disrupt themselves, but we’re well past the point that people said Google couldn’t do AI.
Things are moving quickly right now.
The fourth problem is expressed in the quotes I used above. Mike Krieger and Kevin Weil made similar points last year: when you’re head of product at an AI lab, you don’t control your roadmap. You have very limited ability to set product strategy. You open your email in the morning and discover that the labs have worked something out, and your job is to turn that into a button. The strategy happens somewhere else. But where?
The current market.
This means that most people don’t see the differences between model personality and emphasis that you might see, and most people aren’t benefiting from ‘memory’ or the other features that the product teams at each company copy from each other in the hope of building stickiness (and memory is stickiness, not a network effect). Meanwhile, usage data from a larger (for now) user base itself might be an advantage, but how big an advantage, if 80% of users are only using this a couple of times a week at most?
Result?
In the meantime, when you have an undifferentiated product, early leads in adoption tend not to be durable, and competition tends to shift to brand and distribution. We can see this today in the rapid market share gains for Gemini and Meta AI: the products look much the same to the typical user (though people in tech wrote off Llama 4 as a fiasco, Meta’s numbers seem to be good), and Google and Meta have distribution to leverage. Conversely, Anthropic’s Claude models are regularly at the top of the benchmarks but it has no consumer strategy or product (Claude Cowork asks you to install Git!) and close to zero consumer awareness.
There is much more to his analysis. Definitely worth a read—and some thinking.
Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.
by Gary Mintchell | Apr 6, 2026 | Business, Generative AI
I receive great value from the wisdom and generosity of Seth Godin. He thought out this progression of business value. As you create products and companies and personal value, consider this deeply and seriously—Create value by connecting people.
From Seth Godin Feb 13
The first generation was built on large models, demonstrating what could be done and powering many tools.
The second generation is focused on reducing costs and saving time. Replacing workers or making them more efficient.
But you can’t shrink your way to greatness.
The third generation will be built on a simple premise, one that the internet has proven again and again:
Create value by connecting people.
We haven’t seen this yet, but once it gains traction, it’ll seem obvious and we’ll wonder how we missed it.
Create tools that work better when your peers and colleagues use them too. And tools that solve problems that people with resources are willing to pay for.
Problems are everywhere, yet we often ignore them.
Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.
by Gary Mintchell | Mar 31, 2026 | Generative AI, Podcast
I’ve released a new podcast.
You can subscribe and download from your favorite podcast app or from my site.
It is also available on my YouTube channel.
Episode 275. Why AI in Manufacturing? Why not? I explore how new technologies for knowledge work, unlike in manufacturing, create even more busy work distracting us from our real work–thinking, deep work. Looking beyond the hype, AI tools are going to help us do things. We just don’t know exactly what is best. We must play with the tools to find the best ways to help us.
We also need to consider the limits of text-based LLMs. Researcher Yan LeCun has looked at the limits of these technologies. How can they work for things like bringing a robot into the house, for example, when they are limited to digital and text and the environment is analog. Won’t this take a system of models? Not just one model?
Use them, but don’t be awed. Or bamboozled by CEOs.
This episode is sponsored by Inductive Automation.
Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.
by Gary Mintchell | Mar 31, 2026 | Generative AI, News, Security
Several people involved with standards have shared with me the insight that the driving force for adoption of some of these will come from company boards due to insurance and risk management pressures. Therefore, I found this paper interesting looking at trustworthy AI from the point-of-view of risk management.
Høvik, Norway, 25 March 2026 – New research from assurance and risk management company DNV has identified the foundations to achieving trustworthy artificial intelligence in the context of safety critical industrial processes. According to the paper, Assurance of AI-Enabled Systems, established risk management principles can be adapted to meet the complexity and uncertainty of AI enabled systems. While AI introduces new risks, proven assurance methods from safety critical industries already provide a robust starting point for addressing them
The paper shows that AI reshapes risk because it does not operate as a fixed, predictable component. This makes traditional one‑time assurance insufficient, and highlights the need for continuous and adaptive assurance throughout the lifecycle
Christian Agrell, Programme Director for AI Assurance at DNV, said, “Creating trustworthy artificial intelligence does not require us to start from zero. We already have strong foundations in modern assurance and risk science and our long experience managing digital technologies in high‑risk environments. Applying these principles thoughtfully allows us to build systems that remain safe and reliable, even as they evolve. Trustworthy AI depends on predictable behaviour under uncertainty, and that is exactly what these foundations help deliver.”
The research draws on DNV’s decades-long assurance and risk management experience in critical infrastructure, including the maritime and energy sectors. The foundational principals to create trustworthy AI include:
- A system model that captures the entire AI-enabled system
- This model reflects how AI interacts with humans, digital and physical components, and its operational environment. It enables understanding of emergent behaviour, unintended interactions and context specific risks that cannot be detected by examining the AI component alone.
- Taking a modular approach
- A risk model, applying uncertainty-based assessment and modular risk principles to break down complex systems with their complex and emergent risks into manageable parts across system levels.
- Linking claims to evidence
- These structured arguments connect claims such as “the system is safe” to verifiable evidence, assumptions and rationale. This provides a transparent, auditable framework for demonstrating trustworthiness throughout the lifecycle.
- Continuous, context aware assurance that adapts as AI evolves
- AI-enabled systems change over time as models are updated, data shifts and operating conditions vary. To maintain trustworthiness, assurance must be ongoing rather than a onetime check. This includes real-time monitoring, regular updates to evidence, and reevaluating risks and requirements so that confidence in the system remains valid throughout its lifecycle
“These foundations give industry a clear, actionable way to build and maintain trustworthy AI. We are already working with companies that recognize the potential of AI, as well as the risks it can pose to the critical services they deliver. I urge more organizations to join us in addressing and managing the risks associated with artificial intelligence,” Agrell added.
The position paper is part of DNV’s broader work to help industry adopt AI responsibly and aligns with the company’s recommended practice (DNV‑RP‑0671) for AI assurance.
Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.
by Gary Mintchell | Mar 26, 2026 | Generative AI, Process Control, Software
TwinThread is one of those smallish software companies within an interesting niche that I can’t believe has yet to find a buyer. I quoted noted software developer and LinkedIn commentator Rick Bullota in 2020 extolling the value of the AVEVA/TwinThread link with the AVEVA purchase of OSIsoft. Just last year, I wrote about a stronger partnership between the two.
I see the company has pivoted a bit to now proclaiming itself as “the world’s first to have a complete Industrial AI platform.” I’ll leave that proclamation to your judgement. But this product looks worthwhile to check out.
Last week’s news involved TwinThread releasing an AI-powered manufacturing analytics solution targeting batch processes called Perfect Batch. This product empowers manufacturers to standardize and consistently replicate their best performing or “golden batches”.
Perfect Batch applies industrial AI to dynamically identify ideal batch profiles from historical data and actively recommend actions for increasing efficiency. This enables organizations to rapidly shift from reactive firefighting to proactive optimization in a matter of weeks – not months or years.
Perfect Batch At-a-Glance:
- Rapid Speed to Value: Perfect Batch connects to existing batch execution systems and automatically interrogates past data to build digital twins and apply models in hours.
- Dynamic Perfect Profile Learning: Instead of setting limits manually, Perfect Batch dynamically learns ideal control limits and process centerlines, based on actual process capability and historical performance.
- Unlocked Hidden Capacity: Granular cycle time analysis identifies bottlenecks and lost production time, facilitating capacity improvements from existing assets without new capital investment.
- Optimization by Exception: Automated alerting and issue diagnosis empowers operations teams to focus on solving problems, without getting bogged down with endless troubleshooting and investigations.
- Optional Closed-Loop Action: Thread Builder, a real-time workflow engine that works with Perfect Batch, automates anomaly responses, performs automatic diagnoses, and can trigger specific corrective actions automatically.
- Automated Compliance: Tailored for regulated industries, Perfect Batch provides automated material tracking, quality and yield conformance, and audit-ready histories.
Beyond the plant floor, Perfect Batch helps drive strategic and collaborative alignment across organizations’ entire manufacturing portfolios by providing a global view of asset utilization and batch making performance. As a result, the platform serves as a single source of truth for cross-functional teams. This offers a common lens that operations teams, engineering teams, and supply chain leaders can all use to identify, prioritize, and proactively execute improvement initiatives that optimize the deployment of capital across the supply network.
Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.
