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When Stephanie Downs, cofounder of Uncaged Innovations, a biomaterials startup creating alternatives to animal leathers, learned about the tariffs earlier this year she was forced to add manufacturing outside of the U.S. Since less than 2% of fashion goods are produced in the U.S., all of Uncaged customers are overseas and now enduring a price hike. Like many startup founders, Downs has had to react and adjust operations and business plans based on geopolitical and economic shifts. Over half of small business leaders report negative impacts of changing tariff and trade policies as of this July, according to the WSJ/Vistage Small Business CEO Confidence Index. Just as the pandemic forced massive shifts across sectors, todays founders are navigating a new wave of disruptions: tariff uncertainties, declining federal grants, and changing customer behavior. The same index found that two out of five business leaders are reporting delayed orders, as well as longer sales cycles and a more carefully examined buying process. Downs has experienced similar challenges at Uncaged, with some customers canceling orders due to tariffs on materials shipping into China. Tack and shift These shifts arent always within a founders controlbut how they respond to them is. Companies that simply freeze in uncertainty often dont survive. Those that tack and shift their approach can manage though, and even take advantage, of the changes. At Golden Seeds, after two decades of experience investing in early-stage women-led companies, weve come to view this as swerving and its just as criticalif not more sothan the dramatic course corrections that pivots imply. The art of adapting isnt a one-time decision. Its a continuous process of listening, learning, and iterating. Its important to distinguish between the two concepts. Think of a swerve as proactive responsiveness. Its when startups pick up on early signalslike customer feedback, market shifts, or changes in funding sourcesand adjust accordingly. They are often smaller, tactical shifts that respond to new data. A pivot, in contrast, usually emerges when the product market fit hasnt been clearly determined or the product is no longer viable. Its a deliberate, and often high-stakes, decision to fundamentally change the product, business model, or target market. Why Swerving MattersMaybe More Than Pivoting Every startup swerves. Or at least, every successful one does. It might not be flashy. It doesnt always make headlines. But its the everyday work of managing a company: testing assumptions, talking to customers, analyzing sales patterns, and adjusting accordingly. Its also what investors are often really betting onnot just the initial idea, but the founders judgment and willingness to adjust when reality doesnt match the original vision. Failure to swerve can be fatal. Companies that rigidly stick to the original plan, even when its not working, tend to burn through capital and fade away. Remember, hope is not a strategy. If the company isnt making sales targets, find out exactly why. When the Pivot Is Necessary Still, sometimes swerving isnt enough. When a startup realizes the product isnt viable or the market has evaporated, its time for a true pivot. Take BentoBox, for example. Originally a marketing services platform for restaurants, the company saw opportunityand urgencyduring the pandemic. As dining rooms closed, restaurants needed digital tools for online ordering and payments. BentoBox made a do-or-die shift to become a payments and e-commerce platform, ultimately selling for over $300 million. Lark Health is another notable pivot. Initially a consumer sleep device company, it evolved into an AI-driven nurse platform treating millions of patients struggling with chronic conditions on behalf of health insurers, employers, and PBMs. That transformation didnt happen overnight, but it was a full reinvention that unlocked significant market potential. These examples highlight another common thread: successful pivots often come from companies that were already good at swerving. They were listening, learning, and adapting all alongso when the time came for a bigger move, they were well positioned to act quickly. Great founders and teams are constantly testing, refining, and asking hard questions. They sit in on sales calls. They ask why a customer said no. Is it a product issue? Is onboarding taking too long? Does it require too much manual intervention? Is it too expensive? They look for patterns in whats working and whats not. They make small bets, try new features, and critically, know when its time to either double down or switch gears entirely. They also know how to test demand. In hard tech, for instance, that might mean getting a customer to fund development of a new featurenot just waiting and hoping a sale materializes. That kind of resourcefulness and discipline is what gives a company options when the winds shift. Advice for Investors and Founders For angel investors and board members, supporting a startup goes far beyond capitalit’s about recognizing when a company is at an inflection point and helping the team navigate it with clarity and confidence. That means staying close to the business, asking probing questions, and encouraging founders to test their assumptions early and often. Swervesthose smaller, iterative shiftsshould be a regular topic at the board table, not just in times of crisis. This type of creative and adaptive thinking should be a part of every board meeting. Great investors and advisers recognize that swerving is simply managing a company, its not a sign of failure. And when a pivot becomes necessary, investors can play a critical role in helping leadership assess whether the product, market, or business model needs to changeand how to communicate that shift to employees, customers, and future funders. Great board members challenge, guide, and help founders course correct before the runway runs out. Ultimately, whether you’re advising a tactical swerve or leading a company through a full-scale pivot, the goal remains the same: stay aligned with the market, respond to what the data and customers are telling you, and keep moving forward. In the world of startups, and most specially in this current economy, resilience is importantbut adaptability is essential. The companies that endure and thrive are the ones that listen, learn, and evolveover and over again.
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This year, global EV sales are expected to jump almost 25% compared with 2024. As the demand for electric vehicles soars, theres a looming concern for industry experts: figuring out the best way to repurpose the several-hundred-pound batteries that power these vehicles. According to a 2023 study by McKinsey, the global supply of EV batteries for recycling is steadily increasing and is expected to hit a whopping 7,850 kilotons in 2035. That same year, McKinsey projects that EV battery recycling will be a $7.2 billion industry in the U.S. Currently, though, experts are still trying to find the best way to actually scale the recycling process. The prevailing strategy is a technique that essentially involves shredding EV batteries into a superfine powdera process that has proved costly, complicated, and inefficient. Now, researchers at the Massachusetts Institute of Technology have published a study showing a new way to potentially bypass the shredding step altogether. According to Yukio Cho, lead author on the study and a Stanford energy postdoctoral fellow, the team has developed a new way to build a battery that makes it much easier to separate its component parts, leaving them ready for recycling. The current state of EV recycling The two main ways that EV batteries are diverted away from landfills are through reuse and recycling. Some companies are finding ways to repurpose EV batteries after theyre no longer fit for driving. One startup is using retired EV batteries to power up an entire data center in Nevada, for example, while another is repurposing old batteries to run new EV charging stations. Others are searching for ways to break these batteries down and reuse their valuable components. The current industry standard is to shred the batteries into a fine powder called black mass, which has to be sorted into salvageable metal parts. The sorting process is messy, complicated, and often requires specialized facilities in advanced recycling markets like China to actually make the metals usable. Even then, Cho says, the acids used to sort out the metals can pose an environmental riskand, to top it all off, the whole process is expensive. Elemental components are so complicated, Cho says. Once youve generated this black mass, it’s really difficult to make recovering the critical materials cost-positive. Cho says theres not much consensus among experts today on how many EV batteries are actually getting recycled and how many are being diverted to landfills. What is clear, though, is that theres plenty of motivation to turn EV manufacturing into a more circular economy. To start, siphoning e-waste into trash heaps poses the risk of leaching hazardous materials into soil and water. From an economic perspective, EV batteries also contain valuable metals like nickel, cobalt, manganese, and lithium, which can be harvested and reused to prevent more expensive and polluting ore-mining operations. Imagine an EV battery like a ham sandwich To skirt around the issue of black mass entirely, Cho and his team decided to take a totally novel approach to EV battery design. So far in the battery industry, weve focused on high-performing materials and designs, and only later tried to figure out how to recycle batteries made with complex structures and hard-to-recycle materials, Cho told MIT News in an interview. Our approach is to start with easily recyclable materials and figure out how to make them battery-compatible. A rendering shows (left) the mPEGAA molecule designed by researchers, (middle) how the molecules self-assemble into nanoribbons, and (right) how the molecules are used for the battery electrolyte. [Image: courtesy of the researchers] EV batteries are made of three main parts: the positively charged cathode, the negatively charged electrode, and the electrolyte that shuttles lithium ions between them. Typically, EV batteries are sealed so tightly that, in order to take them apart efficiently, shredding them becomes the best way to recycle them. The novel innovation from the MIT team is a new electrolyte material which, when soaked in an organic solvent, just dissolves like cotton candy, easily separating the batteries parts. Cho compares the innovation to a hypothetical ham sandwich. Imagine that the sandwich has been glued shut, and in order to retrieve the bread, lettuce, and ham, it has been shredded and must be sorted by minute particles. Now, imagine that the sandwich was held together by mayo instead: You could easily separate all of the sandwiches compoents. Thats essentially the difference between the black mass recycling step and the electrolyte process that his team is working on. Chos team created a solid-state battery to test the material, finding that it held up against the batterys demands. Then, once the battery was treated with an organic solvent, the material dissolvedcutting out the necessity of a shredding step entirely. A depiction of batteries made with MIT researchers new electrolyte material, which is made from a class of molecules that self-assemble in water, named aramid amphiphiles (AAs), whose chemical structures and stability mimic Kevlar. [Image: courtesy of the researchers/edited by MIT News] Whats next There are a few shortcomings with the current dissolvable prototype. To start, Cho says the test batterys performance was well below that of todays gold-standard commercial batteries. The performance is at a level that the industry will never think aboutif you have an iPhone 13, youll never think about swapping that for an iPhone 4, Cho says. Matching the performance to the current state-of-the-art batteries is definitely a challenge we haven’t demonstrated yet. Part of that performance deficit, Cho says, likely comes from the fact that his team built its battery from the ground up. While it will be at least several years before this new material might be commercially viable, he believes it could be swapped into future EV batteries without too much hassle on manufacturers parts. I think in the future, we can integrate this material as a part of the battery, Cho says. If you imagine that it dissolves like cotton candy, it can just be a very thin layer somewhere in between the component parts. That will serve the purpose of opening the battery in an autonomous way.
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E-Commerce
Weve spent several years now obsessing over models and assistants, but heres a new interesting truth: the next competitive edge in AI wont be another benchmark, but electrons. And not just any electrons, but cheap ones. As the AI wars heat up, the winners wont simply be those with the best UX or the most compute. Theyll be the firms that can secure abundant low-cost power at scale, hour after hour, year after year. Thats where AI is colliding with the physical world, and where the story stops being about software and starts being about grids, turbines, and price curves. Most recent analyses show that AI-driven data centers are now a visible driver of U.S. electricity demand and are starting to send retail prices higher, a clear signal that the constraint is shifting from graphics processing units (GPUs) to kilowatt-hours. Then theres also a lot of insistence about water, and it deserves some observations. In fact, the problem is that water use is often confused with water consumption. In data centers, much of the water involved in most cooling systems is withdrawn, then used to absorb heat, and later returned. Warmer, yes, but reentering the water cycle after discharge is brought back within permitted temperature ranges. Only some designs (notably evaporative cooling) consume water through vapor losses; others trade water for electricity by leaning on air-cooled chillers or direct-to-chip liquid loops that dramatically cut onsite withdrawals. Think local The right way to think about the problem is local: Water stress is a catchment-level issue, not a global one, and the risk depends on where you site the load and which cooling technology you choose. In short, the headlines often overstate a universal thirst that the engineering and the definitions dont support. None of this, of course, minimizes communities that are water-stressed, where a single facility can matter. Investigations have shown clusters of data centers in arid regions, prompting scrutiny and new local rules. Thats the right debate: match technology choices to basin realities, and stop treating water for AI as the same problem everywhere. In places with abundant non-potable or reclaimed water, or with dry/thermosyphon cooling, the footprint can be managed; in stressed watersheds, it becomes a siting decision, not an engineering afterthought. Electricity is different. There is no local workaround if the price is structurally high. And on cost, the market is brutally clear. The latest Lazard Levelized Cost of Energy+ (LCOE+) report again shows utility-scale wind and solar at the bottom of the price stack, with new gas combined-cycle plants rising in cost and nuclear still the most expensive new build in rich-country conditions. If youre trying to run large training runs or always-on inference, the delta between clean, cheap power and legacy generation is not a rounding errorit is the margin that decides where you build and whether the unit economics make sense. Consider nuclear: Georgias Vogtle expansion finally went online, but only after historic cost and schedule overruns that translated into material rate hikes for customers. If AIs advantage is speed and scale, its hard to square that with technologies that arrive late, over budget, and with levelized costs that sit at the wrong end of the curve. The physics is fine. The economics, today, are not. This is why the new moat isnt access to energy in the abstract: Its access to cheap energy, reliably delivered. The firms that can lock in 24/7 low-cost supply, time-shift non-urgent workloads into off-peak windows, and colocate compute with stranded or overbuilt renewables will win. Everyone else will pay retail, and pass those costs on to users or investors. We are already seeing utilities, grid operators, and tech companies negotiate curtailment and flexibility, and the International Energy Agency’s (IEAs) modeling makes the near-term picture obvious: AI-related demand is rising, and it will test systems that were not designed for this kind of always-on compute. The China factor This brings us to the comparison nobody in Silicon Valley likes to make out loud: China. Look past the coal headlines for a moment and follow the build rates. China hit its 2030 wind-and-solar target in 2024, six years early, and added roughly 429 GW of net new capacity to the grid in 2024 alone, the vast majority wind and solar, backed by massive investment in transmission. Pace matters, because marginal megawatt-hours from ultra-low-cost renewables set the floor for training and inference costs. Chinas grid still has big challenges (curtailment among them), but if youre simply asking Who is manufacturing cheap electrons at scale the fastest? the answer today is not the United States. That doesnt mean resignation; it means focus. If the U.S. wants to stay competitive in AI economics, the priority is not another model announcement: Its a buildout of cheap generation and the wires to move it. Anything that delays that, be it doubling down on gas price volatility, pretending coal is cheap once you factor in capacity payments and externalities, or dreaming of next-gen nuclear that wont arrive on time, will keep AI sited where the power is inexpensive and predictable. In a world of location-aware workloads, electrons decide geography. The takeaway The practical takeaway for companies is straightforward: If you are spending real money on AI, your CFO should now know your blended cost of electricity as intimately as your cloud bill, and should be negotiating for both. Favor regions with abundant wind and solar and strong transmission, insist on time-of-use pricing and demand-response programs, push your vendors on 24/7 carbon-free energy rather than annual offsets that do nothing for peak prices or local loads. None of this is environmental, social, and governance (ESG) posturing. Its cost control for a compute-intensive product line whose unit economics are married to energy markets. On water, keep the conversation precise. Ask for cooling designs, not slogans. Is the system evaporative or closed-loop? Whats the water-use effectiveness and the discharge temperature profile? Where does the site sit on the World Resource Institute’s (WRIs) aqueduct map today and under climate-adjusted scenarios? If your supplier cant answer those basics, theyre not ready to build where youre planning to grow. But dont let the AI is drinking the planet meme obscure a simpler reality: With the right technology and siting, the binding constraint is cheap electricity, not moisture in a recirculating loop. The narrative arc is changing. The first phase of the AI boom rewarded companies that could raise capital and buy a lot of GPUs. The next phase will reward those that can buy electrons cheaply, cleanly, and continuously. If you want a preview of who wins the assistant wars, dont look at the demos. Look at the interconnection queues, the power-purchase agreements, and mostly, the maps of wind and solar buildoutsthe cheapest energy available. Software is glamorous, but power is destiny.
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