Security teams are under constant pressure to do more with the same resources. Manual processes, fragmented tools, and inefficient workflows can slow teams down and pull focus away from what matters most.
In this live webinar, experienced security practitioners share how they’ve escaped the constraints of limited
Researchers have succeeded in developing the smallest fully autonomous robot in history. It measures less than 1 millimeter and can swim underwater for months powered only by light.
According to its new privacy policy, TikTok now collects more data on its users, including their precise location, after majority ownership officially switched to a group based in the US.
We miss the slide rule. It isn’t so much that we liked getting an inexact answer using a physical moving object. But to successfully use a slide rule, you need to be able to roughly estimate the order of magnitude of your result. The slide rule’s computation of 2.2 divided by 8 is the same as it is for 22/8 or 220/0.08. You have to interpret the answer based on your sense of where the true answer lies. If you’ve ever had some kid at a fast food place enter the wrong numbers into a register and then hand you a ridiculous amount of change, you know what we mean.
Recent press reports highlighted a paper from Nvidia that claimed a data center consuming a gigawatt of power could require half a million tons of copper. If you aren’t an expert on datacenter power distribution and copper, you could take that number at face value. But as [Adam Button] reports, you should probably be suspicious of this number. It is almost certainly a typo. We wouldn’t be surprised if you click on the link and find it fixed, but it caused a big news splash before anyone noticed.
Thought Process
Best estimates of the total copper on the entire planet are about 6.3 billion metric tons. We’ve actually only found a fraction of that and mined even less. Of the 700 million metric tons of copper we actually have in circulation, there is a demand for about 28 million tons a year (some of which is met with recycling, so even less new copper is produced annually).
Simple math tells us that a single data center could, in a year, consume 1.7% of the global copper output. While that could be true, it seems suspicious on its face.
Digging further in, you’ll find the paper mentions 200kg per megawatt. So a gigawatt should be 200,000kg, which is, actually, only 200 metric tons. That’s a far cry from 500,000 tons. We suspect they were rounding up from the 440,000 pounds in 200 metric tons to “up to a half a million pounds,” and then flipped pounds to tons.
Glass Houses
We get it. We are infamous for making typos. It is inevitable with any sort of writing at scale and on a tight schedule. After all, the Lincoln Memorial has a typo set in stone, and Webster’s dictionary misprinted an editor’s note that “D or d” could stand for density, and coined a new word: dord.
So we aren’t here to shame Nvidia. People in glass houses, and all that. But it is amazing that so much of the press took the numbers without any critical thinking about whether they made sense.
Innumeracy
We’ve noticed many people glaze over numbers and take them at face value. The same goes for charts. We once saw a chart that was basically a straight line except for one point, which was way out of line. No one bothered to ask for a long time. Finally, someone spoke up and asked. Turns out it was a major issue, but no one wanted to be the one to ask “the dumb question.”
You don’t have to look far to find examples of innumeracy: a phrase coined by [Douglas Hofstadter] and made famous by [John Allen Paulos]. One of our favorites is when a hamburger chain rolled out a “1/3 pound hamburger,” which flopped because customers thought that since three is less than four, they were getting more meat with a “1/4 pound hamburger” at the competitor’s restaurant.
This is all part of the same issue. If you are an electronics or computer person, you probably have a good command of math. You may just not realize how much better your math is than the average person’s.
Even so, people who should know better still make mistakes with units and scale. NASA has had at least one famous case of unit issues losing an unmanned probe. In another famous incident, an Air Canada flight ran out of fuel in 1983. Why?
The plane’s fuel sensors were inoperative, so the ground crew manually checked the fuel load with a dipstick. The dipstick read in centimeters. The navigation computer expected fuel to be in kg. Unfortunately, the fuel’s datasheet posted density in pounds/liter. This incorrect conversion happened twice.
Unsurprisingly, the plane was out of fuel and had to glide to an emergency landing on a racetrack that had once been a Royal Canadian Air Force training base. Luckily, Captain Pearson was an experienced glider pilot. With reduced control and few instruments, the Captain brought the 767 down as if it were a huge glider with 61 people onboard. Although the landing gear collapsed and caused some damage, no one on the plane or the ground were seriously hurt.
What’s the Answer?
Sadly, math answers are much easier to get than social answers. Kids routinely complain that they’ll never need math once they leave school. (OK, not kids like we were, but normal kids.) But we all know that is simply not true. Even if your job doesn’t directly involve math, understanding your own finances, making decisions about purchases, or even evaluating political positions often requires that you can see through math nonsense, both intentional and unintentional.
[Antoine de Saint-Exupéry] was a French author, and his 1948 book Citadelle has an interesting passage that may hold part of the answer. If you translate the French directly, it is a bit wordy, but the quote is commonly paraphrased: “If you want to build a ship, don’t herd people together to collect wood and don’t assign them tasks and work, but rather teach them to long for the endless immensity of the sea.”
We learned math because we understood it was the key to building radios, or rockets, or computer games, or whatever it was that you longed to build. We need to teach kids math in a way that makes them anxious to learn the math that will enable their dreams.
How do we do that? We don’t know. Great teachers help. Inspiring technology like moon landings helps. What do you think? Tell us in the comments. Now with 285% more comment goodness. Honest.
We still think slide rules made you better at math. Just like not having GPS made you better at navigation.
A winter chill descended on the Great Lakes region of North America in January 2026. Some of the effects were apparent in this satellite image as newly formed lake ice and a fresh layer of snow. The image, acquired by the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA’s Terra satellite, shows the region on the morning of January 20, 2026.
In the days prior, a winter storm blanketed many parts of western Michigan near the lake with nearly a foot of snow, according to the National Weather Service. West of Walker, snowfall totals surpassed that amount, reaching nearly 14 inches (36 centimeters). The storm’s effects extended beyond Michigan as well, including blizzard conditions in parts of Ontario east of Lake Huron.
Lake effect snow is common in the Great Lakes area during late fall and winter, occurring when cold air moves over relatively warm, unfrozen water. As the air picks up heat and moisture, it rises to form narrow cloud bands that can produce heavy snowfall.
The air over Lake Erie was still moist enough for clouds to form, though the amount of open water on this lake has decreased sharply in recent days. Around mid-month, during a period of unseasonably warm air temperatures, ice coverage dropped to cover about 2 percent of the lake, according to the NOAA Great Lakes Environmental Research Laboratory. It then spiked to nearly 85 percent on January 21 after temperatures plummeted.
The frigid temperatures were brought about by an Arctic cold front that moved across the region. In Cleveland, for instance, the weather service issued a cold weather advisory on January 19 for wind chills as low as minus 15 to 20 degrees Fahrenheit. On that day, even colder wind chills were reported in the area around Chicago. Forecasts called for another round of cold Arctic air to spill over the Great Plains and Eastern U.S. over the coming weekend, accompanied by heavy snow.
NASA Earth Observatory image by Michala Garrison, using MODIS data from NASA EOSDIS LANCE and GIBS/Worldview. Story by Kathryn Hansen.
Humanoid robots, space travel, the science of aging—Musk weighed in on all of it at this week’s World Economic Forum. But his predictions rarely work out the way he says they will.
It has been an eventful few months for the Northern Hemisphere atmosphere. An unusually early sudden stratospheric warming episode in late November appears to have factored into a weakened and distorted polar vortex at times in December, likely causing extra waviness in the polar jet stream. This helped fuel extensive intrusions of frigid air into the mid-latitudes, contributing to cold snaps in North America, Europe, and Asia, and priming the atmosphere for disruptive winter storms in January.
Russia’s Kamchatka Peninsula has been among the areas hit hard by cold and snowy weather in December and January. More than 2 meters (7 feet) of snow fell in the first two weeks of January, following 3.7 meters in December, according to news reports. Together, these totals make it one of the snowiest periods the peninsula has seen since the 1970s, according to Kamchatka’s Hydrometeorology Center. The onslaught brought Petropavlovsk-Kamchatsky, the regional capital, to a standstill, with reports of large snowdrifts burying cars and blocking access to buildings and infrastructure.
This image, acquired by the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA’s Aqua satellite, shows fresh snow blanketing the peninsula’s rugged terrain on January 17, 2026. Several circular, snow-covered volcanic peaks are visible across the peninsula, one of the most volcanically active areas in the world. Petropavlovsk-Kamchatsky, home to more than 160,000 people, sits along Avacha Bay—a deep, sheltered bay formed by a combination of tectonic, volcanic, and glacial activity.
NASA Earth Observatory image by Lauren Dauphin, using MODIS data from NASA EOSDIS LANCE and GIBS/Worldview. Story by Adam Voiland.
The peninsula in eastern Siberia features rugged terrain with towering, snow-covered peaks and cloud-filled valleys stretching between the Pacific Ocean…
In a chandelier-lit ballroom at Berlin’s Hotel Adlon Kempinski, a quiet revolution is unfolding. Dozens of investors lean in as founders from Zurich, Barcelona, Lisbon and Warsaw pitch a room full of international cannabis investors and the CEOs of the EU’s next cannabis giants. This is a Talman House event, and it’s where European cannabis capital finds its match.
After years of uneven reform, Europe’s cannabis market is finally entering its investment era. As North America wrestles with oversupply and political fatigue, European operators are drawing global attention to their pharmaceutical precision, export potential and growing regulatory stability.
From Albania to Spain and everywhere in between, governments are expanding medical cannabis access and homegrow rights in various forms. In Germany, the new conservative CDU government is cautious on cannabis, but indications suggest they’ll still advance adult-use legalization. Personal possession of up to 25 grams is allowed in public in Germany. Meanwhile, medical supply chains are growing across Western and Eastern Europe through controlled licensing and pilot programs.
Europe remains a frontier defined by both opportunity and red tape. Many deals favor convertible debt or structured instruments over pure equity. Despite the cautiousness, institutional interest is rising. Germany’s Demecan, for example, recently hit a €100 million valuation backed partly by US investors. Last September, Canada’s High Tide purchased a 51 percent interest in cannabis pharma operator Remexian AG with an option to pick up the other 49 percent. Europe’s cannabis infrastructure is maturing and investors are watching closely.
With most national markets still small in scale, the long-term play centers on trading internationally. Companies are positioning themselves to supply EU GMP-certified cannabis and cannabinoid-based pharmaceuticals across the region. While the legal framework is evolving, transparent governance and robust due diligence are non-negotiable for investors. Recent scandals like the collapse of the JuicyFields Ponzi scheme have left many wary.
Among the new generation of European investment platforms, The Talman Group stands out as a credible, selective, membership-based network connecting cannabis and cannabis-adjacent companies with global investors. Its model blends exclusive events in prestigious venues with curated deal sourcing and introductions to sophisticated investors who want compliant and investable opportunities.
In April 2025, Talman hosted more than 140 participants at the Adlon Kempinski in Berlin, where a handful of highly curated companies pitched to investors in a Shark Tank-style setting. As the investment arm of Europe’s largest B2B conference and expo, International Cannabis Business Conference (ICBC), Talman lends a layer of legitimacy to an industry still working to shake its early growing pains in the adolescence of Europe’s cannabis industry.
return on investment: Last April’s Talman House event presented cannabis rockstar DJ Muggs, whose investment portfolio is as impressive as his 35-year music career.
Talman serves as a figurative mentor guiding Europe’s cannabis industry toward maturity. Its role extends beyond matchmaking. The platform provides a buffer of due diligence by screening decks, tracking market intelligence and connecting founders to legal, financial and strategic advisors. Creating the conditions for credible, sustainable industry growth, Talman’s curated network brings much-needed capital into reach for operators bridging a capital desert.
And the opportunity is real in these undercapitalized European markets. Early entrants can enjoy “first mover” status, and if they utilize the head start efficiently, can parlay that into market leadership and delight their investors. Additionally, success in one jurisdiction often opens doors across the EU’s emerging regulatory patchwork. The evolution of policies in Germany, the Netherlands and the United Kingdom could provide compliant, scaled operators with significant upside.
All that said, uncertainty due to evolving regulations continues. Pilot programs have faced repeated delays in the Netherlands and Switzerland. Shallow public markets limit exits, and scandals and bankruptcies remind investors that gaps in oversight can be costly.
Big conglomerates such as British American Tobacco and Tilray entering the space raise the bar for smaller firms as well. Investors have witnessed how market exuberance can outpace fundamentals, but greed is a powerful blinder. Valuation discipline will be essential.
Europe’s cannabis story is unfolding in distinct phases. There are policy harmonization efforts across EU member states, with standardized licensing and quality controls being considered. Simultaneously, cross-border consolidation through multi-country acquisitions and partnerships is already underway. Expect consolidation to pick up speed in 2026 as more North American companies consider Europe’s potential. As the cycle matures, institutional investors will participate by having pension and health funds make cautious allocations. That typically triggers financial innovation (e.g. REITs and special-purpose investment vehicles). Lastly, as compliance standards mature, de-risking through transparency becomes the norm.
Europe’s cannabis investment story isn’t about chasing another green rush. It’s about building the infrastructure that makes the rush possible. Platforms like the Talman Group are helping investors see Europe not as 27 fragmented markets, but as one evolving opportunity. For those willing to navigate complexity, the reward may be as lasting as the reform itself.
This story was originally published in issue 52 of the print edition of Cannabis Now.
Cowork is a user-friendly version of Anthropic’s Claude Code AI-powered tool that’s built for file management and basic computing tasks. Here’s what it's like to use it.
We’re excited to announce a new GeekWire event for 2026: “Agents of Transformation: Inside the AI Shift.” This half-day summit will be held the afternoon of Tuesday, March 24, in Seattle, exploring how agentic AI is reshaping work, creativity, and leadership.
The event, presented by Accenture, features fireside chats, expert panels, and real-world stories from technology leaders, business execs, and others navigating how AI is changing the way we work and lead, from copilots and automation to the rise of intelligent agents.
Tickets are available now, with discounted early bird rates set to end Feb. 24. Speakers will be announced in the coming weeks.
AI agents is the tech industry’s obsession right now, but there can be a big gap between the pitch and the reality. We’re bringing together people who are in the thick of it to talk candidly about what they’re seeing: breakthroughs, challenges, and what comes next.
The event is part of GeekWire’s longstanding tradition of convening tech, business, and policy leaders for insights and new connections. Hosted at one of our favorite Seattle venues, Block 41, the afternoon will include networking opportunities before, during, and after the program, bringing together founders, executives, and technologists from across the region.
It builds on an ongoing GeekWire editorial series, underwritten by Accenture, spotlighting how startups, developers and tech giants are using intelligent agents to innovate.
Even after TikTok removed swastika jewelry from its online shop, I was algorithmically nudged toward a web of Nazi-related products during searches, like “double lightning bolt” and “ss” necklaces.
Waiting on replacement parts can be more than just an inconvenience. It can be a matter of sharp loss of income and opportunity. This is especially true for those who depend on industrial tools and equipment for agriculture and construction. So to keep things run as efficiently as possible, Parts ASAP CIO John Fraser makes sure end customer satisfaction is the highest motivation to get the tech implementation and distribution right.
“What it comes down to, in order to achieve that, is the team,” he says. “I came into this organization because of the culture, and the listen first, act later mentality. It’s something I believe in and I’m going to continue that culture.”
Bringing in talent and new products has been instrumental in creating a stable e-commerce model, so Fraser and his team can help digitally advertise to customers, establish the right partnerships to drive traffic, and provide the right amount of data.
“Once you’re a customer of ours, we have to make sure we’re a needs-based business,” he says. “We have to be the first thing that sticks in their mind because it’s not about a track on a Bobcat that just broke. It’s $1,000 a day someone’s not going to make due to a piece of equipment that’s down.”
Ultimately, this strategy helps and supports customers with a collection of highly-integrated tools to create an immersive experience. But the biggest challenge, says Fraser, is the variety of marketplace channels customers are on.
“Some people prefer our website,” he says. “But some are on Walmart or about 20 other commercial channels we sell on. Each has unique requirements, ways to purchase, and product descriptions. On a single product, we might have 20 variations to meet the character limits of eBay, for instance, or the brand limitations of Amazon. So we’ve built out our own product information management platform. It takes the right talent to use that technology and a feedback loop to refine the process.”
Of course, AI is always in the conversation since people can’t write updated descriptions for 250,000 SKUs.
“AI will fundamentally change what everybody’s job is,” he says. “I know I have to prepare for it and be forward thinking. We have to embrace it. If you don’t, you’re going to get left behind.”
Fraser also details practical AI adoption in terms of pricing, product data enhancement, and customer experience, while stressing experimentation without over-dependence. Watch the full video below for more insights, and be sure to subscribe to the monthly Center Stage newsletter by clicking here.
On consolidating disparate systems: You certainly run into challenges. People are on the same ERP system so they have some familiarity. But even within that, you have massive amounts of customization. Sometimes that’s very purpose-built for the type of process an organization is running, or that unique sales process, or whatever. But in other cases, it’s very hard. We’ve acquired companies with their own custom built ERP platform, where they spent 20 years curating it down to eliminate every button click. Those don’t go quite as well, but you start with a good culture, and being transparent with employees and customers about what’s happening, and you work through it together. The good news is it starts with putting the customer first and doing it in a consistent way. Tell people change is coming and build a rapport before you bring in massive changes. There are some quick wins and efficiencies, and so people begin to trust. Then, you’re not just dragging them along but bringing them along on the journey.
On AI: Everybody’s talking it, but there’s a danger to that, just like there was a danger with blockchain and other kinds of immersive technologies. You have to make sure you know why you’re going after AI. You can’t just use it because it’s a buzzword. You have to bake it into your strategy and existing use cases, and then leverage it. We’re doing it in a way that allows us to augment our existing strategy rather than completely and fundamentally change it. So for example, we’re going to use AI to help influence what our product pricing should be. We have great competitive data, and a great idea of what our margins need to be and where the market is for pricing. Some companies are in the news because they’ve gone all in on AI, and AI is doing some things that are maybe not so appropriate in terms of automation. But if you can go in and have it be a contributing factor to a human still deciding on pricing, that’s where we are rather than completely handing everything over to AI.
On pooling data: We have a 360-degree view of all of our customers. We know when they’re buying online and in person. If they’re buying construction equipment and material handling equipment, we’ll see that. But when somebody’s buying a custom fork for a forklift, that’s very different than someone needing a new water pump for a John Deere tractor. And having a manufacturing platform that allows us to predict a two and a half day lead time on that custom fork is a different system to making sure that water pump is at your door the next day. Trying to do all that in one platform just hasn’t been successful in my experience in the past. So we’ve chosen to take a bit of a hybrid approach where you combine the data but still have best in breed operational platforms for different segments of the business.
On scaling IT systems: The key is we’re not afraid to have more than one operational platform. Today, in our ecosystem of 23 different companies, we’re manufacturing parts in our material handling business, and that’s a very different operational platform than, say, purchasing overseas parts, bringing them in, and finding a way to sell them to people in need, where you need to be able to distribute them fast. It’s an entirely different model. So we’re not establishing one core platform in that case, but the right amount of platforms. It’s not 23, but it’s also not one. So as we think about being able to scale, it’s also saying that if you try to be all things to all people, you’re going to be a jack of all trades and an expert in none. So we want to make sure when we have disparate segments that have some operational efficiency in the back end — same finance team, same IT teams — we’ll have more than one operational platform. Then through different technologies, including AI, ensure we have one view of the customer, even if they’re purchasing out of two or three different systems.
On tech deployment: Experiment early and then make certain not to be too dependent on it immediately. We have 250,000 SKUs, and more than two million parts that we can special order for our customers, and you can’t possibly augment that data with a world-class description with humans. So we selectively choose how to make the best product listing for something on Amazon or eBay. But we’re using AI to build enhanced product descriptions for us, and instead of having, say, 10 people curating and creating custom descriptions for these products, we’re leveraging AI and using agents in a way that allow people to build the content. Now humans are simply approving, rejecting, or editing that content, so we’re leveraging them for the knowledge they need to have, and if this going to be a good product listing or not. We know there are thousands of AI companies, and for us to be able to pick a winner or loser is a gamble. Our approach is to make it a bit of a commoditized service. But we’re also pulling in that data and putting it back into our core operational platform, and there it rests. So if we’re with the wrong partner, or they get acquired, or go out of business, we can switch quickly without having to rewrite our entire set of systems because we take it in, use it a bit as a commoditized service, get the data, set it at rest, and then we can exchange that AI engine. We’ve already changed it five times and we’re okay to change it another five until we find the best possible partner so we can stay bleeding edge without having all the expense of building it too deeply into our core platforms.
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