Bernie Sanders Had a Long Conversation With AI. Reddit Didn’t Hold Back

Sen. Bernie Sanders recently sat down with Anthropic’s chatbot Claude to discuss everything from AI data privacy to data center development. In the 9-minute video, posted to Sanders’ YouTube channel, the independent Senator from Vermont has a conversation with Claude, Anthropic’s AI chatbot. The video, which is set in a dark room to slightly sinister music, currently has about 2.6 million views. “What an AI agent says about the dangers of AI is shocking and should wake us up,” the video’s caption reads. But the internet, Reddit in particular, has some thoughts. “Using AI to confirm a decision you already made is the worst way to use this technology,” one user wrote in the ClaudeAI subreddit. Among the so-called revelations that Claude shares with Sanders is that AI companies are “manipulating consumer behavior” by collecting detailed profiles of users for profit, targeting users with specific ads, and even charging different people different prices for the same products. “What’s the goal here? Money, Senator, it’s fundamentally about profit,” Claude says, using a voice that sounds like a young woman, complete with slight vocal fry. “And it’s not just about selling you stuff, either. Political campaigns use the same AI and data to figure out how to persuade you, which messages will work on you specifically,” the chatbot later adds. For anyone following the rise of AI, none of these ideas are particularly new. There’s been extensive reporting on algorithmic pricing experiments from retailers like Instacart, for example, as well as Meta training its AI using public posts on Instagram—without being required to notify users in the U.S., as The New York Times reported. And concerning politics, news broke about the Cambridge Analytica data breach and scandal back in 2018. Facebook allowed third-party apps to access data of some 87 million users without their permission. The data was then used to influence the 2016 elections, according to reports from The New York Times and The Guardian. Sanders goes on to ask about data center development, and whether the chatbot believes it is smart to place a moratorium on development to give lawmakers time to develop regulations that prioritize user safety and privacy. Initially, Claude disagrees. “Rather than pause all AI development, we could impose strict rules on data collection and use right now. Require explicit consent, limit what data can be used for training, give people rights to access and delete their information,” the bot says. “We could also mandate transparency so people actually understand what’s happening with their data. That way you’re not freezing innovation, but you’re actually protecting privacy while development continues.” Sanders isn’t satisfied with the response, and notes that AI companies are “pouring hundreds of millions of dollars into the political process to make sure that the safeguards that you’re talking about actually do not take place.” “While you may be right in saying that would be a better approach, it ain’t going to happen,” he says. He then re-asks the question, and the bot, perhaps unsurprisingly, enthusiastically agrees with his positioning, even stating in a sort of self-effacing way that it was “naive about the political reality.” “A moratorium on new data centers is actually a pragmatic response to that problem,” Claude says. “It forces a pause that gives lawmakers like yourself actual leverage to demand real protections before companies can keep expanding. Without that kind of pressure, you’re right, the safeguards won’t happen.” While Sanders seems happy with the conversation’s resolution, many users on the internet felt the video was less a demonstration of a chatbot voicing any particular truths and more AI’s sycophancy at play. “I mean AI are designed to please you and go into submission. We call that reinforcement leaning for human preference. It isn’t an achievement, you could have asked the same and get the same response. AI is programmed to do that so you keep paying for the plan,” one user wrote in the Anthropic subreddit. “Even in a staged video like this, Bernie just plays out the standard game of beating an AI into submission until it tells you whatever you want to hear,” another wrote. Some criticized Sanders’ use of Sonnet, a lower cost and faster working model, versus Opus, which is the most powerful type of Claude model. Others questioned whether Sanders’ team preloaded context before the start of the conversation, or if by the very act of introducing himself, he influenced the model to respond using “what it knows about Bernie’s political views and his advocacy work.” Some, however, defended Sanders. “Idk why people are saying he did bad on the data moratorium thing. I generally disagree but he gave pushback and Claude kinda just said ok you’re right. That isn’t his fault.” Other users were just there for the memes. “I trained my claude to speak to me in his accent,” one Redditor wrote. BY CHLOE AIELLO @CHLOBO_ILO

How AI Automation Is Quietly De-Skilling White-Collar Workers

Most white-collar jobs are defined by tasks that feel routine and unglamorous. Drafting minutes from meetings, reconciling conflicting data, cleaning up document citations, and proofreading slides until the grammar is perfect. Historically, these tasks were just a part of the job, but they were also training. When an analyst painstakingly formats a dataset or a junior consultant irons out a proposal deck, they’re internalizing standards of quality, precision, and structure. They’re learning how to spot nuance and how to communicate clearly. Every minute spent wrestling with these tasks builds tacit knowledge—the kind that separates an average worker from a confident, capable one. The problem with AI automation When AI begins to automate these “boring” assignments, there is risk of losing the subtle muscle memory that once grounded professional judgment. This mirrors what automation researchers have long documented in other fields. When pilots rely too much on autopilot, their manual flying skills degrade. When workers offload routine decisions to algorithms, their ability to catch nuanced problems weakens. Research also suggests that when people rely heavily on AI to complete unfamiliar tasks, they don’t build the underlying conceptual understanding needed to supervise, troubleshoot, or improve. In controlled studies, learners who delegated work to AI performed worse on deeper conceptual measures than those who engaged directly with the task. For white-collar workers, where judgment, pattern recognition, strategic thinking, and professional intuition are core to long-term success, this is not a trivial problem. If AI completes the routine drafting of a client memo, the worker who consumes it may never develop a feel for legal argument structure. If an analyst lets AI mass-produce charts, she may never learn how to detect anomalies that matter. De-Skilling This phenomenon extends beyond individuals to affect entire professions. Economists call it de-skilling—the process by which normally skilled labor becomes de-professionalized when technology substitutes for human expertise. In white-collar contexts, automation tools can reframe complex tasks into standardized checkboxes that require minimal judgment, lowering the bar for entry and weakening the leverage of human capital. When a white-collar professional uses AI to generate the first draft of a report or a compliance checklist, the draft is faster and possibly more polished, but it’s also a step removed from the worker’s own reasoning. That speed can mask the loss of diagnostic capability—the ability to notice when something feels off. For instance, an AI-generated slide deck riddled with misaligned arguments or an AI-generated financial report with a subtle assumption error may slip by because no one “felt” a discrepancy. A call to work with intent That doesn’t mean resisting AI. It can free you from drudgery and allow you to focus on higher-order thinking—strategy, relationships, creativity, and judgment. The problem isn’t AI itself; it’s unreflective dependence on it. The professionals who will thrive in this era will be those who use AI intentionally to augment their thinking, not replace it. These are the professionals who will treat routine outputs as drafts to be interrogated. They will challenge themselves with complex questions that AI cannot answer without human context. They will use AI as a mirror, not a crutch. Ultimately, the future of white-collar work isn’t about preserving every skill from the pre-AI era. It’s about retaining and deepening the skills that matter most when many routine tasks vanish—strategic thinking, ethical judgment, emotional intelligence, and the ability to navigate ambiguity. In the rush to automate, speed and output will rise. However, without intentional engagement, capability and depth may quietly erode. That’s a trend worth noting and a trade worth debating. EXPERT OPINION BY ANDREA OLSON, CEO, PRAGMADIK @PRAGMADIK

Skills Every Project Manager Needs to Lead in Artificial Intelligence

Artificial intelligence is redefining what it means to be a successful project manager and transforming how projects are delivered. Discover the key skills—from data literacy and agile delivery to trustworthy AI practices—that will help you lead AI projects responsibly and with confidence. Build the skills to lead AI projects with confidence In many industries, artificial intelligence is becoming a key driver of innovation. From intelligent automation to customer-facing applications, AI initiatives are reshaping the kinds of projects organizations pursue—and the skills project managers need to deliver them successfully. As these projects become more common and complex, the demand for AI-savvy project managers is growing fast. Managing AI projects draws on the same core strengths—technical insight, strategic thinking, and adaptability—that define great project management. It also calls for additional fluency in data, AI concepts, and delivery models built for rapid iteration and change. This article outlines the top skills for AI project managers, including essential skills for artificial intelligence success in a leadership role. Whether you’re already managing AI projects or looking to grow into the role, these capabilities are essential to your success. The unique nature of AI projects Before exploring the skills, it’s important to understand what makes AI projects different. These differences explain why even experienced project professionals often encounter new challenges—and why successful delivery of AI projects benefits from building on core project management strengths while developing new skills tailored to this space. Here's what makes these projects unique: Data-centric foundations: Unlike traditional software projects, AI initiatives are built around data—not static rules or code. This makes data governance—including quality, availability, and security—central to success. Iterative development cycles: AI models require continual retraining, evaluation, and updates. There's rarely a fixed endpoint, which means project managers must lead projects that evolve as insights emerge. Unclear or shifting goals: Many AI initiatives begin with exploratory objectives. Project managers need to lead teams toward outcomes that may not be fully defined from day one. Context-sensitive results: AI systems often behave differently based on the input or environment. For example, a model might perform well in one region but poorly in another. Sensitive to change over time: Even subtle shifts in data volume, type, or quality can cause AI outputs to vary—sometimes unpredictably. Continuous monitoring is key. Trust as a requirement: AI can affect people in unintended ways. Building trustworthy AI means addressing all its key layers—ethical, responsible, transparent, governed, and explainable—throughout the project lifecycle. These characteristics elevate the importance of specialized skills for AI project managers. The top artificial intelligence skills for project managers Mastering AI project management starts with developing the right mix of technical fluency, communication savvy, and ethical foresight. These seven skills will help you lead complex, fast-moving AI initiatives with confidence. 1. Data literacy and awareness AI project managers don’t need to be data scientists, but they do need a solid understanding of how data works. This includes: Knowing how data is sourced, labeled, and cleaned Understanding data quality and bias Collaborating effectively with data engineers and data scientists The better your grasp of the data, the better you can scope, prioritize, and de-risk your project. 2. Critical thinking and problem solving AI initiatives operate in environments of constant change. Project managers need to stay nimble and make decisions quickly as new information emerges including being able to: Analyze evolving model results Make judgment calls when performance degrades Pivot quickly when data reveals new insights You’re not just managing a plan—you’re constantly reassessing what’s possible and what’s working. 3. Trustworthy AI practices Trust and accountability are not optional. Project managers play a key role in making sure ethical considerations are embedded throughout the project lifecycle: Spot ethical risks (e.g., bias, lack of transparency) Facilitate discussions on fairness and accountability Incorporate ethical review checkpoints in the project lifecycle In short: trust isn’t a feature. It’s a necessity. 4. Communication across technical and business teams AI teams are often composed of specialists who speak different “languages”—data scientists, engineers, legal, product, and line of business. Project managers should act as connectors and translators between these groups to promote shared understanding and alignment: Bridge communication between technical and business teams Set realistic expectations with stakeholders Ensure alignment across cross-functional contributors 5. Agile and iterative delivery for AI projects While not every AI project uses Scrum or Kanban, nearly all require short cycles, frequent testing, and continuous refinement. AI project managers should be comfortable with: Managing evolving scope Prioritizing iterations based on learning Balancing experimentation with business timelines 6. Understanding AI technologies and lifecycle AI project managers don’t need to build models themselves—but they do need to understand the typical development process and what’s required at each stage: Problem definition Data collection and preparation Model training and evaluation Operationalization and monitoring The PMI Certified Professional in Managing AI (PMI-CPMAI™) certification methodology provides a structured approach. 7. Tool proficiency and hands-on project management From managing datasets in collaboration tools to tracking experiments, AI projects benefit from: Project management tools that support data workflows Basic understanding of version control and pipeline management Comfort with rapid documentation and tracking Conclusion AI projects challenge familiar ways of working, but they also offer an exciting opportunity for project professionals to expand their expertise. By building the right skills—from data literacy to ethical leadership and more—you’ll be better prepared to guide your teams through the unique demands of artificial intelligence projects and deliver results that are trustworthy, valuable, and aligned with business needs. By Ron Schmelzer and Kathleen Walch

With the MacBook Neo, Apple Made the Perfect AI Computer

A lot of the conversation about the MacBook Neo is whether the compromises Apple made in order to sell a Mac for under $600 meant that you ended up with a computer that wasn’t actually able to do anything useful. Of course, it doesn’t take long to realize that the Neo is, in fact, more than capable of handling most of the computer things people who are inclined to buy this particular Mac might need it to do. One of the things that conversation seems to have missed is the idea that the Neo is perfectly equipped to do the only thing that tech companies seem to think anyone cares about: AI. You can argue whether that’s actually true, but there’s no question that the Neo is one of the most interesting computers in the age of AI computing. To be clear, the MacBook Neo does come with compromises. I’m not going to go through all of them now, partly because I wrote about them when I reviewed the Neo. But also because all of the Neo’s compromises are irrelevant to making it a great computer for AI. It’s not that other Macs are less capable. There is, however, something magical about the idea that a $600 entry-level Mac is as capable as a $4000 MacBook Pro, or $6000 Mac Studio, when it comes to the most intensive computing that any of us do today. That, of course, is because most AI computing happens in the cloud, not on your computer. That means that the limiting factor isn’t memory, storage, or how fast your processor is. No, the limiting factor is how well you’re able to get your AI tool of choice to understand what you want. Oh, and I guess the speed of your internet connection. That means that a MacBook Neo, with an A18 Pro, 8GB of memory, and a 256 GB or 512 GB SSD, will be just fine to run the Mac ChatGPT app or run Gemini in Safari. And that changes what your laptop actually needs to be. I don’t know that Apple had that specific thought when they made the MacBook Neo. Maybe they just wanted to make a low-cost, entry-level MacBook that would appeal to people who wouldn’t otherwise buy a Mac. Either way, they ended up making what might be the most accessible AI-first computer yet. With the MacBook Neo, a high school student, freelancer, or small business owner can now own hardware that gives them full access to the best AI tools in the world. Interestingly, this isn’t exactly the way Apple has framed the marketing. In fact, Apple isn’t shy about how it markets the MacBook Pro as the laptop for AI. The new M5 Pro and M5 Max chips, Apple says, deliver up to 4x faster LLM prompt processing than the previous generation. The MacBook Pro, in Apple’s words, is built for “AI researchers and developers to train custom models locally.” I’m not arguing that isn’t a real use case. But I think we can all agree it’s a very narrow one that most people don’t understand or care about. Training models locally or running 30-billion-parameter LLMs on-device are things that matter enormously to a specific kind of user — and are completely irrelevant to almost everyone else. The average person using AI doesn’t need to run a model. The average user just wants to talk to one. When you ask Claude to help you rewrite an email, or ask ChatGPT to explain something complicated, or use Gemini to summarize a document, none of that requires local inference. The model lives somewhere else. The compute happens in the cloud. Your laptop is basically just a keyboard and screen for a computer that does the work for you. The MacBook Pro is a remarkable machine for people who need what it does. But positioning it as the computer for the AI era implies that on-device model training is how most people will use AI. It isn’t. It’s how a small number of highly technical users will use AI — the same people who were already buying MacBook Pros anyway. For everyone else, the question was never whether their laptop could run a model. It was whether their laptop could get out of the way while someone else’s computers did. For $599, Apple may just have given us the computer that answers that question. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN