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We know artificial intelligence can replace some person-to-person interactions, but can it also create opportunities for us to connect? Building personal connections through networking is becoming an even more pivotal part of the job search. As of last year, an estimated 84% of companies use an employee referral program while recruiting and 30% of referred candidates get hiredcompared to 7% of general applicants, according to research from Erin Technologies, an employee referral-based recruitment platform. In todays difficult job market, where over 7 million people are currently unemployed, according to the Bureau of Labor Statistics, networking experts say using AI tools to draft emails and LinkedIn messages can help you get a referral that brings your application to the next levelas long as you use them the right way. Networking isnt just about who you know. Its about who knows you, says Jan Tegze, author of the book Job Search Guide: Be Your Own Career Coach. In a crowded job market, that makes a huge difference, he says. If you want to use AI to step up your networking, experts have some tips. Use AI to find people to add to your network Finding a company that you want to work for or a job listing that seems like a good fit is half the battle. But when it comes to getting the job, making connections with a broad range of people who can give advice and refer you for a job is key. Still, it can be difficult to know who to reach out to. The mistake that a lot of people make is they reach out to the recruiter, says Matt Landau, CEO of AI job search site Swooped. Recruiters are inundated with those types of messages, and they often dont have any type of hiring sway, he says. One solution is to use AI tools that search for people working in roles similar to the ones you are interested in or at companies you are applying to. Some AI networking tools even find peoples LinkedIn profiles and business emails for you, making connecting with new people a much speedier process. However, Landau notes the importance of respecting the privacy of people you are reaching out toeven if their contact information is public. Sticking with career-focused social media sites such as LinkedIn and avoiding personal emails or phone numbers is a much better way to make a first impression. Ive gotten Instagram DMs, and Twitter, things of that sort, Landau adds. I think thats a little bit of an invasion of privacy and might rub people the wrong way. Delegate repetitive and time-consuming tasks to AI AIs power is in its ability to sort through information and generate outputs quickly, freeing up time for other work. In fact, 89% of employees said using AI leads to fewer repetitive tasks, according to a Morning Consult survey commissioned by Zoom in 2023. Networking experts suggest leveraging these benefits of AI by using it to create custom resources for your job search, such as organized lists of people to reach out to and templates or drafts for emails and LinkedIn messages. I encourage my clients to use AI in their job search like a PA for their job search process, says Sarah Felice, executive and career coach at Prima Careers. Felice emphasizes, though, that AI is better as a personal assistant rather than a director. It can augment everything you do but should never replace your style of writing or your research, she adds. Personalize AI-generated messages before you send them out Once AI helps get the networking process started with an organized list of people to connect with and drafted messages or templates, its important to take a step back and add personal touches before hitting send. A lot of times we’ll find people writing very generic [messages], like Hey, Im interested in this role. I’d love for you to take a look at my resume, Landau says. That often is not going to spark interest. Instead, networking experts say you should add information about yourself that will interest potential connections. This could be information emphasizing why you would be a good fit for an open role, relevant accomplishments, or just things you have in common with the person. I think whats very important is to embrace your background, says Jonathan Javier, CEO of AI job search site Wonsulting. People who come from the same background as you understand your story. For AI tools specifically created to help job seekers network, Javier says a good test of quality is seeing if the tool prompts users to add relevant information like this and edit drafts as part of the process. If you send the same AI-written note to 10 people, theyll notice and probably wont reply, Tegze adds. The point of networking is to build trust. AI can help you get started, but it cant build real relationships for you. AI helps most when you understand its limits AI has drastically changed the job-seeking process in recent years, but its not the first major technological change. Networking experts suggest learning from past shifts to understand how to alter your networking process. I like to remind my clients that I remember life before LinkedIn, when the sort of information it provides so easily was not available, Felice says. AI is clearly the next evolution in technology that we are only just seeing the beginning of today. When using this emergent technology, remembering the meaning behind the tasks you do is critical. And for networking, the name of the game is connecting on a person-to-person basisnot crafting perfect networking requests. Once AI has helped you connect with someone, let your own words shine in further communications, such as during informational calls. Asking questions about the persons role and getting advice on how to look for a job in their field or at their company can build a relationship that is both personally and professionally valuable. When someone helps you, its not because your message was perfectly written, Tegze adds. Its because they believed you were genuine and worth helping. And no AI can fake that for you.
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If you want to understand how even modern American cities became hostile to human life, dont start with the political conspiracies; look at the way city planners and road engineers calculate success. Every day, public agencies across the country greenlight projects that cost millions of dollars, destroy neighborhoods, and ultimately kill peopleall in the name of saving drivers a few seconds. This is standard operating procedure, justified by a single, dangerous metric: vehicular delay. In transportation bureaucratese, its called Level of Service (LOS). Think of it as a report card with grades A to F describing how freely cars move. But this grade has nothing to do with safety, quality of life, economic productivity, or human flourishing. Its entirely about how long a vehicle waits at an intersection or slows down during rush hour. The built environment is shaped around that metric. {"blockType":"creator-network-promo","data":{"mediaUrl":"","headline":"Urbanism Speakeasy","description":"Join Andy Boenau as he explores ideas that the infrastructure status quo would rather keep quiet. To learn more, visit urbanismspeakeasy.com.","substackDomain":"https:\/\/www.urbanismspeakeasy.com\/","colorTheme":"salmon","redirectUrl":""}} Take a look at this table that status quo planners and engineers use to measure an intersections performance. [Image: courtesy of the author] Experts give a failing grade to an intersection where people wait a little over a minute before going about their business. Taxpayers are forced to chip in for road expansion projects that cost hundreds of millions of dollars to buildprojects justified by the impatient nature of drivers. It gets worse. The intersections are graded during the busiest hour of the busiest day of a week. If the experts were honest about their analysis, theyd tell you the following: During the busiest hour of the day, the average driver waits 30 seconds at the stop sign. We give that a grade of D. The other 23 hours of the day dont matter. The average person attending a public hearing has been trained since early childhood that A is good and F is bad. So even if they dont like the idea of the local government seizing their front yard in order to widen a street to improve LOS, normies assume road expansions are for the greater good. Such is the treacherous nature of LOS. But wait, it gets even worse. Fortune-telling replaces advanced education and engineering judgment as the experts responsible for designing transportation systems use manuals that are the equivalent of Magic 8 Balls to ask if we need more space for car traffic decades into the future. The answer, of course, is always Signs point to yes. Again, if they were honest, the forecasting analysis would be described like this: The transportation department is guessing that 20 years from now, the average driver might have to wait an entire minute at the stop sign. We give that a grade of F. The other 23 hours of the day wont matter. With the prophecy of more traffic in hand, engineers not only design todays streets to earn good grades on the pseudoscience report card, they design for a future they cant possibly predict. With infrastructure designed for high-volume, high-speed, low-delay motor vehicles, anyone wanting to walk or ride a bike is put into a lethal game of Frogger. When a city does create bus and bicycle infrastructure to shift trips from vehicles to other modes, the traffic report cards dont reflect the fact that people have options. Its solely focused on cars. Theres no redeeming quality to LOS. This obsession with delay has had disastrous effects. When your only metric is vehicle throughput, you end up designing highways through neighborhoods, not places worth living in. A traffic engineer will tell you its more efficient to eliminate street parking and narrow sidewalks to make room for a dedicated right-turn lane. The spreadsheet says so. But that same design makes it harder to cross the street, harder to linger, harder to be a human being. The math checks out, the morality doesnt. A status quo success story is when a road expansion allows a driver to get home 18 seconds sooner but makes it impossible for a child to safely bike to the library. Every single intersection is analyzed and graded based on seconds of delay for drivers. So every single intersection needs more lanes to pump as much car traffic as quickly as possible through an intersection. Anything else (moms pushing strollers, grandparents with canes, kids on bicycles) interferes with LOS. Here are three important questions for experts to ponder: Is slow-moving car traffic ever safer than fast-moving traffic? Do we have any obligation to provide safe and convenient access for people when they arent inside cars? What are the economic downsides of wider, faster streets in the central business district? When planners and engineers truly wrestle with those questions, they can choose to remain a conformist who ignores the damage of traffic metrics, or become an outlier in the industry and make a positive impact that might be felt for generations to come. Things can get better in the end. {"blockType":"creator-network-promo","data":{"mediaUrl":"","headline":"Urbanism Speakeasy","description":"Join Andy Boenau as he explores ideas that the infrastructure status quo would rather keep quiet. To learn more, visit urbanismspeakeasy.com.","substackDomain":"https:\/\/www.urbanismspeakeasy.com\/","colorTheme":"salmon","redirectUrl":""}}
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The AI copyright courtroom is heating up. In back-to-back rulings last week, the ongoing legal war between AI companies and content creators has significantly shifted, ostensibly favoring the former. First, Anthropic got the better outcome of a case that examined whether it could claim “fair use” over its ingestion of large book archives to feed its Claude AI models. In another case, a federal judge said Meta did not violate the copyright of several well-known authors who sued the company for training its Llama models on their books. At a glance, this looks bad if you’re an author or content creator. Although neither case necessarily sets a precedent (the judge in the Meta case even went out of his way to emphasize how narrowly focused it was), two copyright rulings coming down so quickly and definitively on the side of AI companies is a signalone that suggests “fair use” will be an effective shield for them, potentially even in higher-stakes cases like the ones involving The New York Times and News Corp. {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/mediacopilot-logo-ss.png","headline":"Media CoPilot","description":"Want more about how AI is changing media? Never miss an update from Pete Pachal by signing up for Media CoPilot. To learn more visit mediacopilot.substack.com","substackDomain":"https:\/\/mediacopilot.substack.com\/","colorTheme":"blue","redirectUrl":""}} As always, the reality is a little more complicated. The outcomes of both cases were more mixed than the headlines suggest, and they are also deeply instructive. Far from closing the door on copyright holders, they point to places where litigants might find a key. What the Anthropic ruling says about AI inputs vs. outputs Before I get going, I need to point out that I’m not a lawyer. What I offer here is my analysis of the cases based on my experience as a journalist and media executive and what I’ve learned following this space for the past two years. Consider this general guidance for those curious about what’s going on, but if you or your company is in the process of arguing one of these cases or thinking about legal action, you should consult a lawyer, preferably one who specializes in copyright law. Speaking of, here’s a little refresher on that: Copyright law is well defined in the U.S., and it provides for a defense for certain violations, known as fair use. Almost all of the AI companies at the forefront of building models rely on this defense. Determining whether a fair-use defense holds water comes down to four factors: The purpose of the use, or whether it was for commercial or noncommercial purposes. Courts will be more forgiving for the latter, but obviously what the AI companies are doing is a massively commercial exercise. This also covers whether the allegedly violating work is a direct copy or “transformative.” Many have said that AI outputs, because they aren’t word-for-word copies and usually rely on many different sources, are transformative. The nature of the copyrighted work: More protection usually goes to creative works than factual ones. AI systems often deal with both. How much of the original work was copied: Reproducing short excerpts is usually OK, but AI companies typically ingest entire works for training. Courts have sometimes tolerated full copying as long as the output doesnt reproduce the entire work or big chunks verbatim. Whether the violation caused market harm: This is a large focus in these cases and other ongoing litigation. The outcome of the Anthropic case drew some lines between what was OK and what wasn’t. The fact is, anyone can buy a book, and for the books that were legally obtained, the judge said that training its AI on them qualified as fair use. However, if those books were illegally obtainedi.e. piratedthat would amount to a copyright violation. Since many of them undoubtedly were, Anthropic might still pay a price for training on the illegally copied books that happened to be in the archives. An important aspect of the Anthropic case is that it focuses on the inputs of AI systems as opposed to the outputs. In other words, it answers the question, “Is copying a whole bunch of books a violation, independent of what you’re doing with them?” with “No.” In his ruling, the judge cited the precedent-setting case of Authors Guild, Inc. v. Google, Inc. from 2015. That case concluded Google was within its rights to copy books for an online database, and the Anthropic ruling is a powerful signal that extends the concept into the AI realm. However, the Google case came out in favor of fair use in large part because the outputs of Google Books are limited to excerpts, not entire books. This is important, because a surface-level reading of the Anthropic case might make you think that, if an AI service pays for a copy of something, it can do whatever it wants with it. For example, if you wanted to use the entire archive of The Information, all you’d need to do is pay the annual subscription. But for digital subscriptions, the permission is to access and read, not to copy and repurpose. Courts have not ruled that buying a digital subscription alone licenses AI training, even though many might read it that way. The missing piece in the Meta case: harm The Meta case has a little bit to say about that, and it has to do with the fourth point of fair-use defense: market harm. The reason the judge ruled in favor of Meta was because the authors, which include comedian Sarah Silverman and journalist Ta-Nehisi Coates, weren’t able to prove that they had suffered a decline in book sales. While that gives a green light for an AI to train on copyrighted works as long as it doesn’t negatively affect their commercial potential, the reverse is also true: content creators will be more successful in court if they can show that it does. In fact, that’s exactly what happened earlier this year. In February, Thomson Reuters scored a win against a now-defunct AI company called Ross Intelligence in a ruling that rejected Ross’s claims of fair use for training on material derived from Thomson Reuters’ content. Ross’s business model centered around a product that competed directly with the source of the content, Westlaw, Thomson Reuters’s online legal research service. That was clear market harm in the judge’s eyes. Taken together, the three cases point to a clearer path forward for publishers building copyright cases against Big AI: Focus on outputs instead of inputs: It’s not enough that someone hoovered up your work. To build a solid case, you need to show that what the AI company did with it reproduced it in some form. So far, no court has definitively decided whether AI outputs are meaningfully different enough to count as “transformative” in the eyes of copyright law, but it should be noted that courts have ruled in the past that copyright violation can occur even when small parts of the work are copiedif those parts represent the “heart” of the original. Show market harm: This looks increasingly like the main battle. Now that we have a lot of data on how AI search engines and chatbotswhich, to be clear, are outputsare affecting the online behavior of news consumers, the case that an AI service harms the media market is easier to make than it was a year ago. In addition, the emergence of licensing deals between publishers and AI companies is evidence that there’s market harm by creating outputs without offering such a deal. Question source legitimacy: Was the content legally acquired or pirated? The Anthropic case opens this up as a possible attack vector for publishers. If they can prove scraping occurred through paywallswithout subscribing firstthat could be a violation even absent any outputs. The case for a better case This area of law is evolving rapidly. There will certainly be appeals for these cases and others that are still pending, and there’s a good chance this all ends up at the Supreme Court. Also, regulators or Congress could change the rules. The Trump administration has hardly been silent on the issue: It recently fired the head of the U.S. Copyright Office, ostensibly over its changing stance on AI, and when it solicited public comment on its AI action plan, both OpenAI and Google took the opportunity to argue for signing their interpretation of fair use into law. For now, though, publishers and content creators have a better guide to strengthening their copyright cases. The recent court rulings don’t mean copyright holders can’t win, but that the broad “AI eats everything” narrative won’t win by itself. Plaintiffs will need to show that outputs are market substitutes, the financial harm is real, or that the AI companies used pirated sources in their training sets. The rulings aren’t saying dont suethey show how to sue well. {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/mediacopilot-logo-ss.png","headline":"Media CoPilot","description":"Want more about how AI is changing media? Never miss an update from Pete Pachal by signing up for Media CoPilot. To learn more visit mediacopilot.substack.com","substackDomain":"https:\/\/mediacopilot.substack.com\/","colorTheme":"blue","redirectUrl":""}}
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