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Theres now an e-bike that can be powered with the same charger you use for your MacBook. But like many innovations in the electric space, if youre in the U.S. youll have to wait to get your feet in the pedals. The new bike is from Estonian e-bike company Ampler, which bills it as the worlds first USB-C chargeable e-bike. It comes in two models, the Nova and Nova Pro, both currently available for preorder at a cost of roughly $2,800 and $3,300, respectively. The first batch is expected to ship in June. No need to carry around a bulky, custom, bike-specific power block, Kristjan Maruste, chief technology officer at Ampler, says in a YouTube video announcing the Nova. Charge your bike wherever you areat home, on the go, or even in the office. The addition of a USB-C port is a smart move that could make e-bikes more accessible (even allowing them to serve as supplementary phone chargers on the go). As of right now, though, the Nova bike is available only to customers in the U.K., EU, and Switzerlandand, given that the Trump administrations new tariffs will likely hit the American e-bike industry hard, it could be quite some time before USB-C chargeable e-bikes pop up here. [Photo: Ampler Bikes] “A secret race” Most e-bikes on the market today require a special charger designed to work with the voltage of the bikes battery and plug-in design. Often, these chargers are patented by the company selling the bike and designed so that the bikes are compatible only with the companys chargers. This can present a few challenges, including the fact that riders have to remember to pack the charger if they expect to run down the battery, and that many chargers come with a large attached power block. Chargers also add to the bike’s overall cost: The Trek Hyena Gen 2 charger costs $85, for example, while the Bosch 4A standard charger costs $135.50. To address these issues, Ampler designed its Nova bikes with a USB-C port incorporated directly into their frame. Its the same kind of charger thats compatible with devices like the iPhone 16e, MacBook Pro, and most wireless headphonesmeaning you probably already have one lying around. The bike’s port also works in the opposite direction, letting you top off your phones battery on the go. This idea began at one of our company hackathons back in 2020, when we asked ourselves the question, Why cant e-bikes be charged with laptop chargers? Maruste says in the announcement video. According to Kaja Aulik, brand and campaign marketer at Ampler, it turned out that many other companies had theorized, planned, and tried to implement this swap, but e-bike technology hadnt caught up yet. Luckily, she says, many engineers at Ampler had already worked to create the first USB-C chargeable e-scooter, so there was a solid base of experience to start with. The main challenge in implementing the USB-C charger was ensuring that the technology actually worked quickly and smoothly enough. The finished product takes three hours to fully charge and has a range of 50 to 100 kilometers, depending on road conditions. (That’s on the quicker side for a lithium-ion e-bike battery, which typically takes four to seven hours to charge to 100%.) It was a secret race against companies way larger, older, and wealthier, Aulik says. Nimbleness and agility were important to keep in mind. Uncertain terrain for American e-bikes This new development comes as American e-bike production is on uncertain terrain, to say the least. On Wednesday, President Donald Trump announced tariffs of 125% on Chinese imports. By some estimates, more than 90% of e-bikes sold in the U.S. are either fully assembled in China or contain Chinese-made parts. Even brands that manufacture their e-bikes on American soil still rely on parts shipped from China and other countries. On top of the burdensome new tariffs, Trumps administration has also removed the de minimis loophole that allowed products under $800 to be imported from China without tariffs, adding yet another extra cost for bike manufacturers. Amid this landscape, its going to be significantly harder (and more expensive) to ship e-bikes to the U.S. and to build them here. As American e-bike companies work just to hit ther bottom line, it might be some time before they get around to innovating on their charger portsmeaning consumers in the U.S. will be using traditional e-bike chargers for the foreseeable future.
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E-Commerce
AI is an extraordinary tool that amplifies our cognitive capacity. It can analyze, summarize, and generate content faster than any human. However, AI is only ever as good as the questions we ask it. It will never replace our capacity for thinking, and can, in fact, reinforce bias because it is learning what we teach it. For this reason, the top skills of the future include thinking skills. According to the World Economic Forums Future of Jobs report, employers anticipate that beyond technical literacy, the most in-demand capabilities will be creative thinking, critical thinking, resilience, and the capacity for learning. Thinking is a premium, and yet it is also the very thing that is most at risk. We all know that when it comes to data, rubbish in = rubbish out. The same goes for our mind. What we feed it and how we use it determines the quality of our contribution and the value we add. As a high-performance coach and leadership expert, I spend my time consulting with leaders and their teams, challenging them to do better thinking and extract the value of their collective capacity. Modern-day workers are facing a triple threat from the joint epidemics of algorithms, attention theft, and burnout. Heres why: 1. Algorithms reinforce biases More and more, our capacity to think, create, and problem-solve is being challenged by algorithms delivered through social media. Our viewpoints are being regurgitated back to us via algorithms that sense what we like, what we tolerate, and what we think we need. Social media serves to reinforce existing beliefs, not challenge them. We are slowly losing the capacity for critical thinking, and this is the very capacity we need to develop if we are to remain adaptive in a world where cognitive load is being managed more and more by computers. 2. Attention theft robs us of time Attention theft is catastrophic to independent thinking and crippling our ability to focus. How many notifications are pinging right now to pull your attention away from reading this article? How many times a day are you pulled away from the task at hand? Research from Tania Barney, neuroscience and sensory processing expert, suggests that distractions are costing us time as well as money. Her research found: An average of 2.1 hours are lost daily as a result of distractions. The average time spent on a task before we get distracted is 11 minutes. The average time it takes after a distraction to return to a task is 25 minutes. After meetings, emails, unplanned interactions, and rest breaks, how many hours do we have left in a day for thinking and productive work? We get pulled into the urgent things that feel pressing but do not meaningfully matter (like chats with colleagues, reply-all emails, and notification alerts). All this leads to the next major threat to thinkingburnout. 3. Burnout robs us of energy Burnout is the compound interest on lost productivity due to attention theft. Just because were getting distracted by urgent unimportant stuff doesnt mean that the real important stuff goes away. It piles up, weighing us down psychologically and eating into recreation hours where we should be recharging our batteries through rest, exercise, or time with loved ones. Burnout is a global issue, costing humans their well-being and businesses millions in lost productivity. Burnout is the result of prolonged work stress. Symptoms include overwhelm, constant exhaustion, and a feeling of being ineffective at work no matter how hard you try. Increased rates of burnout add up to bad news for business. Burnout has been identified as one of the leading causes driving people to leave their jobs. But it also leads to disengagement, which can cost employers 34% of a disengaged employee’s annual salary, according to the Gallup State of the Global Workplace 2021 report. Prioritize team thinking time to leverage collective potential With the joint epidemics of algorithms, attention theft, and burnout, our most precious resources have changed from time and money to energy and attention. To counteract this, we all need to be more curious. Promote and legitimize thinking time by asking more questions in daily interactions. Encourage your team members to build on one anothers ideas. Create regular cadences when the team meets to reflect, reprioritize, and reset, such as quarterly team-planning workshops. The future of work is humanand your capacity to create spaces and places where people can think, learn, adapt, and grow is what will allow teams and organizations to transform and endure.
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E-Commerce
With their drab gray suits and their Buddy Holly glasses, the so-called traitorous eight don’t look like revolutionaries. Given no context, you can imagine them occupying some kind of middle-management role at a small regional bank. And yet these are the people you can thank for the digital world. The eightwhich included Intel cofounder Gordon Moorehad departed Shockley Semiconductor Laboratory to found Fairchild Semiconductor, which soon became the world’s biggest producer of electrical components for computers. Many of its founders would, in turn, leave again to launch their own ventures. Many of these companies coalesced in the same areathe place we now call Silicon Valleycreating an ecosystem for innovation and technological development that endures to this day. Look again at that photo. Even with the suits and the glasses, these are arguably some of the most interesting and influential people that the technology industry has ever known. Even if you don’t know their names, and even though they have never appeared on the Joe Rogan podcast, they still have a legacy that endures to this day. The Power of Hardware On a personal level, I have always found hardware more interesting than software. There’s a joke that CPUs are just “rocks we tricked into thinking,” which has some truth to it. You can’t help but be amazed at the process that turns iron, copper, gold, and silicon dioxide into something that can run unfathomably complex mathematical calculations, play chess, and stream Netflix. And that’s before you take into account that even the most basic consumer CPU has billions of transistors, each measuring a fraction of the width of a hair. Perhaps the main reason I’m drawn to hardware is that it’s often easy to measure whether something is better than the thing that preceded it. With a tape measure, you can see whether one computer is smaller than another. You can calculate how many mathematical operations a CPU can perform in a second, or count the number of pixels on a display. You can measure its weight, or the heat it gives off, or whether one battery has a larger capacity than another. Hardware is clean-cut. Straightforward. Unambiguous. And these improvements aren’t theoretical, but are felt directly by the end user. When a physical object is meaningfully better, you can tell. If you have upgraded from an Intel to an Apple Silicon Mac, you know this. You probably remember what it was like when you ditched your bulky CRT monitor for an LCD flat panel. You know the difference between a computer with a mechanical hard drive and one with flash storage. Hardware is typically built with utility in mind. The old adage “hardware is hard” is true, but it neglects the fact that it’s also pretty expensive. You only really build something if you believe it’s better than the existing thing, and that somebody will find it useful enough to pay for it. Silicon Valley Needs to Rediscover Its Roots The modern tech industryespecially that which now occupies the same hallowed ground once trod by the treacherous eighthas become a shell of its former self. Techs innovations feel only marginally iterative at best. It is this that makes me nostalgic for the era when Silicon Valley was about siliconor, more specifically, physical, tangible objects that changed the world. And I believe it is an era that we can, and must, return to. The Silicon Valley of the late 1960s, 1970s, and 1980s was a glorious time of American innovation and engineering, where verifiable geniuses discovered the breakthroughs that allowed our current world to exist. The integrated circuit. The microprocessor. The computer mouse. It was an era when technological vision and clear-thinking business strategy combined to bring new inventions to a market, and then popularize them to a global scale. And in doing so, Silicon Valley changed everything. To be clear, I am not just talking about vision. I’m talking about hardware. The applications we will need to run in the future will require faster, better computers, and we need somebody to invent them. Faster, better computers will allow us to reclaim ownership of the tech we use, enabling us to finally break free of the cloud. It will help undo some of the disastrous cultural changes that have occurred over the past decade or so, when people got used to the idea that they must always be subject to the mercies of another, larger tech company. Hardware is hard. Change is even harder. But in this case, I think it’s worth it. The Bright Light on the Tip of the Spear So, there is some cause for optimism, and its not in giant GPUs. Buried in the news coming out of CES was the announcement of Nvidias DGX Spark, a $3,000 desktop computer powered by Nvidias GB10 Grace Blackwell Superchip, that went relatively unnoticed but I believe is a significant moment in personal computing. The DGX Spark delivers up to 1 petaflop of performance in a compact form factor, giving researchers and developers unprecedented access to cutting-edge computational power directly at their desks. Its like having a computer thats a thousand times faster than a regular desktop in the body of a Mac Mini, and Im a little surprised it isnt being taken more seriously. In more human terms, Nvidia created an ultrapowerful Mac Mini that developers, data scientists, and AI researchers are able to use to run reasonably large data workloads and AI models on their desk as opposed to a fleet of massive GPU servers in the cloud. While Silicon Valleys biggest companies have grown on the back of software, the truth is that it needs hardware to grow any further, and while those GPUs might be the headline-grabbers during Nvidias earnings, creating meaningful new kinds of computing is what will lead to actual innovation in software. As a result, by creating a Blackwell chip inside a Mac Mini-size supercomputer, Nvidia allows companies to crunch through large data sets or run self-hosted generative AI models quickly and efficiently, all without relying on the cloud to do so. This vastly lowers the barrier to entry for high-performance computing, which currently requires buying or renting expensive specialized hardware or spinning up expensive infrastructure. I’m going to dive briefly into why this matters. For years, both at Voltron Data and previously at BlazingSQL, I’ve advocated for clustering smaller, more efficient, and less-expensive GPUs together using high-performance networking. However, network limitations have always prevented full utilization of the cluster’s compute performance since data simply couldn’t move fast enough to keep GPUs fully fed. While it hasnt shipped yet, Nvidia has specifically called out the inclusion of ConnectX to allow users to connect two Nvidia DGX Spark computers together, as well as other features (NCCL, RDMA, GPUDirect storage) that are specifically built for faster networking. This will enable efficient parallel processing and high-bandwidth communication, making high-performance AI and analytics workloads accessible to a broader range of researchers and enterprises. A distributed model using a cluster of Nvidia DGX Spark units could offer a more cost-effective and flexible alternative to currentl available GPU clusters, lowering the barrier to entry for basically any high-performance computing use cases. My focus on Nvidia DGX Spark is to illustrate a greater point about what will keep Silicon Valley at the forefront of technological progress. True innovation doesnt come from just making things bigger or more powerful, but in the distinct relationship and interactions between software and hardware, and even between different pieces of hardware. Nvidia DGX Spark isnt just Nvidia making a chip smaller, but finding ways to add faster on-device memory, software to make getting the data to both the memory and the GPU faster, and (I imagine) some unique ways to keep it cool. The truly world-changing innovations and technological breakthroughs that will advance humanity will come from a deep commitment to silicon engineering, and Silicon Valley needs to remember that this is the only way that software will continue to grow.
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E-Commerce
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