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The nations electronic vehicle charging network is getting more reliableand Tesla Superchargers are leading that advancement. To get more people into electric vehicles, we need a robust EV charging network. That doesnt just mean having accessible chargers; it means public chargers must be reliable, offer a good customer experience, and can’t be too expensive. For years, public chargers have been plagued with reliability issues. A 2024 study found that one in five EV charging attempts at public stations fail. Imagine if you go to a traditional gas station and 2 out of 10 times the pumps are out of order, study lead Omar Asensio told Harvard Business School at the time. Consumers would revolt. President Donald Trump has also thrown the state of the EV charging network into question. In February, he paused funding for the National Electric Vehicle Infrastructure (NEVI) program, cutting off millions of dollars for EV chargers. (This week, Trump allowed NEVIs $5 billion in funding to keep flowing to states, with revised guidelines.) Still, despite that pause, the EV charging network has been growing. And theres been progress with the state of those public EV chargers, according to J.D. Power, which today released findings from its 2025 U.S. Electric Vehicle Experience Public Charging Study. ‘Non-charging visits’ are declining The top takeaway is that the EV charging network is the most reliable its been in years. Just 14% of EV owners say they visited a charger without successfully charging their vehicle. A non-charging visit, as J.D. Power calls it, could occur because of wait times at public chargers, but the majority of such visits happen because the charger is out of service or not working properly. Thats down five percentage points from 2024, and marks the lowest level in four years. It also matches the all-time low since J.D. Power began tracking these visits in early 2021. Tesla Superchargers have the lowest percentage of failed charging visits, at just 4%. Other charging companies including Electrify America (6%), Red E (10%), and EVgo (12%) were all below that average for failed visits as well. Tesla chargers: Satisfaction is high but declining Tesla also leads the EV charging companies in terms of customer satisfactionbut that satisfaction has dipped. On a 1,000-point scale, Tesla Superchargers have a customer satisfaction with a score of 709the top of the J.D. Power study, but also a 22-point drop from last year. Other EV charging networks, including the Mercedes-Benz Charging Network, Rivian Adventure Network, and Ford Charge collectively earned a satisfaction score of 709 as well, but these werent ranked in the study because of their limited footprint. J.D. Power measures satisfaction across multiple factors, like speed, the physical condition of charging station, things to do while charging, safety, cost, and how easy it is to pay. ‘Very easy to use’if you drive a Tesla Teslas drop in satisfaction seems to come mostly from non-Tesla owners. Tesla has facilitated an experience for its owners by creating an optimal technical environment that makes the charging process very easy to use and complete payments, says Brent Gruber, executive director of the EV practice at J.D. Power. That process isnt quite as streamlined for non-Tesla owners. Non-Tesla owners using the Superchargers are particularly less satisfied than Tesla owners with the cost of charging; non-Tesla drivers are often charged a higher per-kilowatt-hour rate. Tesla has faced a challenging year, with sales plunging as EV drivers look to distance themselves from CEO Elon Musk. Tesla owners have also been trading in their EVs at all-time high levels. Charging costs are a concern for EV drivers across vehicle and charging types, though. Satisfaction with charging costs for both Level 2 and DC fast chargers dropped to 459 (down 16 points) and 430 (down 16 points), respectively, on a 1,000-point scale. Some of that may be because new fast charging networks kept their prices low as they built out their presence, and because free charging was sometimes a perk for new EV purchases. Electricity rates have also been rising across the country, which affects EV charging prices.
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With artificial intelligence progressing at a rate that can generously be described as unprecedented, many students considering college are wondering what a traditional degree may have to offer in these rapidly changing times. In fact, AI skills and background education were top of the list of factors taken into account as LinkedIn assembled its first-ever list of top colleges, which was released on August 12. While the list also prioritizes other categories culled from its dataincluding hiring rates, job mobility, and alumni networksthe special breakout category that many will surely be most interested in is the one that shows the highest percentages of grads entering the workforce in AI-focused jobs. Many grads are probably concerned about the ability of traditional four-year programs to equip them for this new job landscape, which is understandable as most professionals are currently unclear about how it will impact their companies futures. According to LinkedIn, 81% of C-suite executives are favoring job candidates that are comfortable with AI tools. Which schools are most AI focused? The California Institute of Technology (Caltech) comes in at the top of two lists, including the highest percentage of recent graduates going into AI fields, and the highest percentage of new grads listing AI literary and engineering skills on their LinkedIn profiles. LinkedIn also found that Caltech offers a range of AI-focused classes and overall programs, including AI4Science initiative, which the website describes as an initiative aimed at bringing AI researchers with experts from other disciplines to push modern AI tools into every area of science and engineering. The Massachusetts Institute of Technology (MIT) came in second in both categories, with University of California-Berkeley, Stanford, Carnegie Mellon University, Harvey Mudd, and University of California-San Diego also appearing on both lists. Schools with the highest percentage of recent grads going into AI occupations California Institute of Technology Massachusetts Institute of Technology Carnegie Mellon University Harvey Mudd College University of California-Berkeley Stanford University University of Rochester University of California-San Diego University of Chicago Harvard University Schools with the highest percentage of recent grads adding AI-related skills on LinkedIn profiles California Institute of Technology Massachusetts Institute of Technology Harvey Mudd College Stanford University Carnegie Mellon University University of California-Berkeley University of California-San Diego Brown University Georgia Institute of Technology Princeton University While these two breakout categories are particularly relevant for those considering tech fields, the larger list looks at more traditional considerations for students, including job placement, recruiter demand, career success, network strength, and knowledge breadth. They also specify notable skills listed by grads from the top schools. For the overall list, Princeton took home the top spot, with Duke and the University of Pennsylvania taking the second and third spots. The entire list can be found on LinkedIns website.
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E-Commerce
Ten years from now, it will be clear that the primary ways we use generative AI circa 2025rapidly crafting content based on simple instructions and open-ended interactionswere merely building blocks of a technology that will increasingly be built into far more impactful forms. The real economic effect will come as different modes of generative AI are combined with traditional software logic to drive expensive activities like project management, medical diagnosis, and insurance claims processing in increasingly automated ways. In my consulting work helping the worlds largest companies design and implement AI solutions, Im finding that most organizations are still struggling to get substantial value from generative AI applications. As impressive and satisfying as they are, their inherent unpredictability makes it difficult to integrate into the kind of highly standardized business processes that drive the economy. Agentic vs. Interpretive Agentic AI, which has been getting tremendous attention in recent months for its potential to accomplish business tasks with little human guidance, has similar limitations. Agents are evolving to assist with singular tasks such as building websites quickly, but their workflows and outputs will remain too variable for large organizations with high-volume processes that need to be predictable and reliable. However, the same enormous AI models that power todays best-known AI tools are increasingly being deployed in another, more economically transformative way, which I call interpretive AI. And that is whats likely to be the real driver of the AI revolution over the long term. Unlike generative and agentic AI, interpretive AI lets computers understand messy, complex, and unstructured information and interpret it in predictable, defined ways. Using much of the same IT infrastructure, the emerging technology can power large organizations complex processes without requiring human intervention at each step. Use cases Some interpretive AI applications are already in use. For example, doctors are saving significant time by using interpretive AI tools to listen to conversations with patients and fill in information on their electronic health record interfaces to track care and facilitate billing. In the near future, the technology could determine fault in auto accidents based on police reports written in any of thousands of different formats, or process video recorded from a laptop screen as someone edits a presentation to provide teammates with an automated update on work completed. The applications are wide-ranging and span all manner of industries. Based on estimates for areas such as coding and marketing where generative AI is most applicable, interpretive AI could unlock 20% to 40% productivity gains for the half of GDP that comes from large corporations. First, though, they must commit to developing a comprehensive, long-term strategy involving multiple business functions and careful experimentation, and change entrenched processes and work culture norms that slow its adoption. Done right, the obstacles are surmountableand the payoff could be massive. A different application of generative AI models One of the most basic drivers of economic growth is the ongoing effort to standardize and scale up a particular process, making it faster, cheaper, and more reliable. Think of factory assembly lines enabling mass production, or the internets codification of computer communication protocols for use across disparate networks. Generative AI has been, on the whole, disappointing when it comes to automation. For example, many firms have tried to use generative AI chatbots to reduce the time their human resources staff spends answering employees questions about internal policies. However, the open-ended output from such systems requires human review, rendering the labor savings modest at best. The technology seems to inherit much of the unpredictability of humans along with its ability to mimic their creative and reasoning skills. Agentic AI promises to do complicated work autonomously, with smart AI agents developing and executing plans for achieving goals step-by-step, on the fly. But again, even when agents become smart enough to help a typical knowledge worker be more productive, their outputs will be quite variable. Enter interpretive AI. For the first time, computers can usefully process the meaning of human language, with all its nuance and unspoken context, thanks to the unprecedentedly large models developed by firms like Open AI and Google. Interpretive AI is the mechanism for using the models to exploit this revolutionary advance. Until now, computers ability to capture, store, aggregate, summarize, and evaluate a large organizations activities were limited to those that were easy to quantify with data. Interpretive AI can quickly and precisely execute these functions for many other important activities, at a vast scale and at minimal marginal cost. For instance, no longer will businesses need manual processes to monitor and manage levels of activity and progress in knowledge-worker tasks such as coding a feature into a software solution or developing a set of customer-specific outreach strategies, which usually require dedicated middle management staff to collect information. Companies can make productivity gains by using interpretive AI for a range of other previously hard-to-measure employee issues as well, including the tone and quality of their interactions with customers, their cultural norms in the workplace, and their compliance with office policies and behavioral expectations. Transforming the management of knowledge work The use of interpretive AI will enable the widespread transformations that unlock newly efficient ways of working at large organizations (which are responsible for organizing and producing most of the worlds goods and services). It will dramatically reduce the need for extensive, costly, slow-moving, and unenjoyable middle management work to coordinate complex interrelated programs of activities across teams and disciplines. Even better, it can efficiently understand operationally vital but opaque aspects of how work happens, such as the decades worth of legacy code and data that make even minor technology process changes time-consuming and challenging for any long-lived enterprise. Of course, interpretive AI is not mutually exclusive with generative and agentic AIagain, its simply a different way to use the powerful models that power those technologies. A decidedly unsexy way, certainly, but for businesses looking for ways to maximize the economic impact of AI over the next ew years, its just the unsexy they need.
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E-Commerce
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