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.
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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.
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Om Malik wrote about a new marketing phenomenon—velocity.
This is how the new announcement economy works. You declare a massive number. The headlines write themselves. The stock moves. Mission accomplished. Whether the deal actually closes becomes almost irrelevant. The momentum already happened. Remember Stargate?
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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.
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.
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I have been involved with recycling since the mid-80s. I hate waste—whether as in Lean or as in throwing stuff away. This news came to me from a company called Woodchuck—a clever play on words since they recycle wood. Also a good example of effective use of AI.
Grand Rapids, Michigan – March 24, 2026 – Woodchuck, the AI-powered climate-tech startup redefining how construction and manufacturing industries handle wood waste, today announced a joint sustainability initiative with Walbridge, one of the nation’s top industrial and automotive constructors. The program supports Ford Motor Company’s construction waste-reduction efforts at its new manufacturing facility in Marshall, Mich.
In the first three months, the program has given teams a clearer view of the materials being discarded, diversion rates, cost reductions, and operational efficiency — already achieving 40% of the project’s projected materials-related savings. This early progress offers Walbridge a powerful solution to address customers’ waste management needs and lays the groundwork for a new standard operating procedure for future large-scale construction projects.
A Legacy Builder Confronts a Modern Waste Challenge
For more than a century, Walbridge has delivered some of the most complex automotive and industrial projects in North America. As Walbridge’s customers expand their sustainability commitments, construction waste management is a growing priority — particularly on megaprojects where the volume and variability of materials can shift daily.
On the Ford project, wood waste quickly emerged as one of the most unpredictable waste elements. Crating, dunnage, international shipping pallets, and custom rigging arrived in wide-ranging sizes and material types, creating a diverse and constantly changing waste stream.
These complexities revealed opportunities for innovation. Walbridge saw the potential to elevate efficiency, reduce hauling expenses, and strengthen alignment with Ford’s sustainability goals. The need for real-time visibility into container levels and the makeup of each load became a catalyst for adopting a smarter, data-driven solution — one that made waste handling more predictable, cost-effective and sustainable.
“Our partnership with Woodchuck is built on collaboration. Transparent and real-time communication allows our team to adapt quickly to changing material waste streams on the ground. Detailed information about each load provides complete visibility not only into what is diverted from a landfill, but also into its end destination and intended use, delivering transparency and enabling measurable sustainability outcomes,” said Sander Mathijs, Walbridge Sustainability Manager. “Another key program feature is its ability to scale, allowing us to calibrate capacity and scope to meet the waste‑diversion needs of the project.”
Woodchuck’s AI Platform Delivers Immediate, Scalable Impact
Woodchuck.ai leverages its AI platform across the Ford project to track, report and validate the diversion of wood, cardboard, plastic, and metal; all with minimal onsite labor and seamless integration into Walbridge’s existing workflows.
Walbridge saw meaningful improvements within the first quarter diverting thousands of tons of wood, cardboard, plastic and metal; reducing waste, reducing landfill dependency, and reducing costs. Over the course of the project, Woodchuck will divert 8,000 tons of wood and 1,000 tons of cardboard, plastic, and metal from landfills.
Woodchuck’s detailed reporting also strengthens accountability, giving Walbridge clear data documenting recycling and reuse for both internal tracking and customer sustainability documentation.
Because the Woodchuck platform is designed for large, multi-phase construction programs, the improvements seen at the Marshall project can be replicated at scale. Whether deployed on a single megaproject or rolled out across multiple sites, contractors gain the same visibility, control, and cost efficiencies, making the solution a powerful model for nationwide waste management and sustainability performance.
“Our partnership with Woodchuck has been a game-changer,” said Ross Linton, Group Vice President, Walbridge. “In just a few short months, they’ve helped us transform our waste process to one that’s measurable, trackable, and easily managed. Our team is empowered to plan ahead, driving efficiency and sustainability. We’re excited about the future possibilities this collaboration brings.”
Creating a New Standard for Future Walbridge Projects
Based on early results, Walbridge expects the Woodchuck-enabled process to become a foundation for future large-scale builds across automotive, manufacturing, technology, and advanced industrial sectors.
“Walbridge is demonstrating what it looks like when a contractor treats waste as a strategic input rather than an afterthought,” said Todd Thomas, CEO of Woodchuck. “By embracing real-time data, AI-enabled insights, and a commitment to measurable sustainability outcomes, they’re proving that smarter waste management isn’t just good for the environment — it’s good for productivity, cost efficiency, and project certainty. Their leadership on Ford’s Marshall project shows what’s possible when innovation becomes part of the construction workflow, and they’re setting the pace for how the industry will operate going forward.”
About Woodchuck
Woodchuck is a climate impact start-up dedicated to empowering contractors, manufacturers, and biomass energy producers by streamlining wood waste diversion and processing. We are committed to leveraging advanced AI technologies to transform waste into valuable resources, reduce landfill usage, and provide a steady, sustainable supply of biomass. Based in Grand Rapids, Michigan, Woodchuck is funded by an investor syndicate led by Mason Fink, Beckett Industries, NorthStar Clean Energy and Alloy Partners. For more information, visit https://woodchuck.ai/.
About Walbridge
Walbridge is one of America’s largest privately held construction companies founded in Detroit in 1916. The company offers construction management, engineering, and real estate services for customers in manufacturing, hyperscale data centers, automotive, defense, higher education, health care, and government. Walbridge employs more than 1,500 professionals in North America. Visit www.walbridge.com or connect with us on LinkedIn to learn more.
Process Description
Woodchuck uses AI in two fundamentally different—but tightly connected—ways: at the job site and in the data layer. Together, they turn what was once an opaque, manual waste process into a real-time, measurable system.
1. AI at the Job Site: Shifting Sorting to the Beginning. Traditionally, construction waste sorting happens after the dumpster is full—if it happens at all.
That process is:
Manual and labor-intensive
Expensive to perform at scale
Logistically inefficient
Often skipped entirely
The result? Most mixed construction debris—especially wood—ends up in landfills, even when it could have been reused or converted into energy.
Woodchuck flips this model.
Instead of waiting until the end, Woodchuck uses AI-enabled image recognition at the point of disposal:
As materials are placed into dumpsters, cameras and sensors identify what’s being thrown away
The system distinguishes wood from other materials in real time
It guides proper usage of containers and flags contamination early
This front-end sorting approach changes everything:
Reduces contamination before it becomes a problem
Eliminates the need for costly post-collection sorting
Increases diversion rates dramatically (from <30% to >95%)
Ensures clean wood streams that can be converted into renewable biomass
In short, AI moves sorting from a reactive, end-of-process activity to a proactive, in-the-moment decision.
2. AI in the Data Layer: Turning Waste into Intelligence
Once materials are collected, Woodchuck’s platform continues to track and analyze everything that happens next. This is where the second layer of AI comes in: data aggregation, modeling, and reporting.
Through its dashboard, Woodchuck provides construction companies, developers, and asset owners with full visibility into their waste streams, including:
Material tracking
Exactly how much wood was collected, where it came from, and how it was processed
End-of-life transparency
Clear documentation showing where the material went—whether to biomass facilities or other reuse pathways
Carbon impact metrics
Precise calculations of:
CO₂e emissions avoided from landfill diversion
Carbon benefits from renewable energy generation
Energy output conversion
How much renewable energy was produced from their waste (e.g., BTUs generated, equivalent homes powered)
Operational insights
Trends across projects, contamination rates, and opportunities to improve efficiency
This transforms waste reporting from a rough estimate into a verified, auditable dataset—something increasingly critical for:
ESG reporting
Regulatory compliance
Winning sustainability-driven bids
Internal performance benchmarking
3. From Waste Management to a Measurable System
What makes Woodchuck different is not just the use of AI—it’s where and how it’s applied:
At the edge (job site): AI drives behavior change and improves material quality in real time
In the platform (dashboard): AI converts operational data into financial, environmental, and strategic insights
The result is a closed-loop system where:
Waste is captured correctly from the start
Materials are tracked through their full lifecycle
Outcomes are quantified and reported with precision
Construction companies no longer have to guess what happened to their waste—or treat it as a cost center.
They can see it, measure it, and increasingly, use it as a source of savings, energy, and competitive advantage.
Check out the sidebar ad about the Carbon Almanac. Written and edited by a hundred volunteers, this book contains many ways to help solve the carbon waste problem.
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