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Vice-president Luis de Guindos says Washington’s volatile trade policies and reduced co-operation also threaten stabilityView the full article

Curiosity is one of the most consequential forces in human history. Every scientific breakthrough, technological leap, and cultural advance begins not with knowledge, but the desire to know. At its core, curiosity drives us to close the gap between what we know and what we want to know, a cognitive itch triggered by uncertainty and resolved through learning and the pursuit of meaning. Curiosity as an evolutionary advantage Early humans who explored their environments, experimented with tools, and learned from novel stimuli were more likely to secure resources, avoid threats, and pass on their genes. As a result, curiosity became embedded in our biology, reinforced by neural reward systems that make learning intrinsically pleasurable. In line, neuroscientific research shows that curiosity activates the brain’s dopaminergic pathways, the same circuits involved in motivation and reward, which explains the positive correlation between curiosity and impulsivity. When we encounter a gap in our knowledge, we experience a mild form of cognitive discomfort. Resolving that gap produces satisfaction, reinforcing future exploration. In that sense, curiosity is a built-in, biologically coded feedback loop for learning. But evolution also imposed constraints. Curiosity, like any adaptive trait, is beneficial only within limits. For example, there are many scenarios in which too much exploration could prove fatal. A hunter-gatherer wandering too far from their tribe risked encountering predators or hostile groups. In such contexts, restraint was adaptive. Curiosity had to be expressed with caution. This tension between exploration and exploitation remains with us today. We are wired both to seek novelty and to prefer predictability. The familiar is efficient but boring; the unknown is exciting but costly. History offers similar patterns. During periods of intellectual repression, such as the Inquisition, curiosity was actively punished, making individual inquiry dangerous. By contrast, the Enlightenment celebrated curiosity as a virtue, unleashing scientific and philosophical progress. The same underlying human drive manifested differently depending on cultural conditions. Curiosity, then, is universal, but its expression is highly variable. The paradox of AI: A triumph that threatens its own foundation Fast forward to the present, and we are witnessing one of humanity’s greatest achievements: artificial intelligence. The convergence of mathematics, computer science, and data has enabled machines to simulate aspects of human cognition. We have, in effect, built systems that can approximate, emulate, and even surpass our thinking. Even if AI stopped evolving tomorrow (which seems unlikely), its implications are already profound. AI can augment human capability, accelerate problem-solving, and democratize access to knowledge. It functions as a cognitive copilot, allowing individuals to perform tasks that once required entire teams. But every technological advance also carries unintended consequences. And in the case of AI, the risks are not just economic and ethical, but psychological, too. Specifically, AI threatens to erode curiosity. The erosion of curiosity in the age of artificial certainty Curiosity depends on uncertainty. It requires a gap between what we know and what we want to know. AI, by design, collapses that gap. When answers are instantly available, prepackaged, and delivered with confidence, the motivation to explore diminishes. Why struggle with a problem when a machine can solve it in seconds? Why engage in deep learning when surface-level understanding is sufficient to get by? This is what I have described elsewhere as “artificial certainty.” AI does not just provide answers; it creates the illusion that we understand them. The output is coherent, fluent, and persuasive. But coherence is not comprehension. The result is a shift from active to passive cognition. We consume knowledge rather than generate it. We outsource thinking rather than exercise it. A useful analogy is physical fitness. Imagine a world where machines do all the lifting for you. Your muscles would atrophy. The same applies to the mind. Curiosity is a mental muscle, and like any muscle, it weakens with disuse. In this sense, AI is the equivalent of a microwave for ideas. It delivers fast, convenient results, but often at the expense of depth and craftsmanship. We move from “slow thinking,” which is effortful and reflective, to “fast consumption,” which is effortless but shallow. There is also a linguistic irony worth noting. “Deep learning,” once a human aspiration, is now primarily associated with machines. Meanwhile, human learning risks becoming increasingly superficial, if not dormant. To be sure, such concerns may ultimately prove overstated, as they often have in the past. Socrates, after all, warned that writing would erode memory, fearing that reliance on external tools would weaken internal capacities. Yet history also suggests that overcorrection is safer than complacency. There are, in fact, good reasons to be vigilant: When effort is removed from the learning process, engagement tends to decline; when answers are readily available, the incentive to question diminishes; and when cognition is outsourced too readily, the underlying skills can atrophy. The point is not to resist technological progress, but to ensure that convenience does not quietly displace the very mental habits that made such progress possible in the first place. What the science says about cultivating curiosity If curiosity is both essential and at risk, the obvious question is: Can it be developed? The answer is yes, but not in the simplistic way often suggested. Curiosity is influenced by both stable traits and situational factors. While some individuals are naturally more curious than others, environments and habits play a critical role. Before diving into what to do, it is worth pausing on a more basic question: How curious are you, really? The first step is a proper self-assessment. Not the flattering version you might hold of yourself, but a more objective view grounded in data and external feedback. Curiosity is closely linked to well-established personality traits, particularly openness to experience, one of the Big Five, which captures intellectual curiosity, imagination, and a preference for novelty. Science-based assessments can provide a reliable baseline here. So can 360-degree feedback, which often reveals a gap between how curious we think we are and how we are experienced by others. Even informal input from colleagues, friends, or mentors can be illuminating, especially when it highlights whether you ask thoughtful questions, challenge assumptions, or genuinely engage with new ideas. Equally important is specificity. Curiosity is not a uniform trait. People are rarely equally curious about everything. Reflect on where your curiosity naturally shows up and where it does not. You may be deeply inquisitive about ideas but indifferent to people, or fascinated by technology but incurious about history, culture, or opposing viewpoints. Mapping these patterns matters, because developing curiosity is not about becoming universally interested in everything. It is about understanding your blind spots and deliberately expanding into areas where your instinct is to disengage. First, intrinsic motivation matters. Studies grounded in self-determination theory show that curiosity flourishes when individuals feel autonomous, competent, and connected to others. In practical terms, this means people are more curious when they pursue topics that genuinely interest them, rather than those imposed externally. The implication for organizations is clear: forced learning rarely produces genuine curiosity. Second, exposure to novelty is key. Curiosity thrives on diversity of input. Interacting with people from different backgrounds, disciplines, and perspectives increases the likelihood of encountering information gaps. This is why interdisciplinary environments are often more innovative. They create friction between ideas. Third, habits of reflection enhance curiosity. Research on learning and memory suggests that active engagement, such as writing, teaching, or debating, deepens understanding and sustains curiosity. Passive consumption, by contrast, leads to the illusion of knowledge without real insight. Fourth, time allocation matters. Curiosity requires cognitive space. In environments dominated by urgency and efficiency, there is little room for exploration. Scheduling time for reading, thinking, and unstructured inquiry is not a luxury; it is a necessity. Fifth, tolerance for uncertainty is crucial. Individuals with a high need for cognitive closure prefer quick answers and are less likely to engage in open-ended exploration. Developing comfort with ambiguity, through practices such as Socratic questioning or deliberate exposure to complex problems, can enhance curiosity. Finally, there is evidence that curiosity can be trained through small behavioral interventions. For example, prompting individuals to generate questions before receiving answers increases engagement and retention. Similarly, framing tasks as puzzles or challenges can activate curiosity-driven motivation. These findings align with the broader argument that curiosity is not a fixed trait but a dynamic capability shaped by both internal and external factors. The role of leaders in modeling curiosity While individual strategies matter, curiosity is ultimately a social phenomenon. It is shaped, amplified, or suppressed by cultural norms. From early childhood, curiosity is not simply an individual trait but a product of developmental context. Parents, teachers, and early environmental experiences play a decisive role in shaping how, and whether, curiosity endures into adulthood. Research in developmental psychology shows that children whose caregivers respond contingently to their questions, encourage exploration, and tolerate uncertainty tend to develop higher levels of intrinsic curiosity. Conversely, environments that emphasize compliance, correct answers, and performance over inquiry can suppress exploratory behavior over time. Educational studies also find that classroom climates prioritizing rote learning and standardized outcomes often erode students’ natural inquisitiveness, even when baseline curiosity is high. Longitudinal evidence suggests that these early patterns persist, shaping adult tendencies toward intellectual risk-taking, openness, and lifelong learning. In short, curiosity is cultivated or constrained early, but its trajectory can be reinforced or reversed later, especially through social and organizational contexts. This is where leadership becomes critical. Leaders set the tone for what is valued. If they prioritize certainty, speed, and efficiency above all else, curiosity will decline. Employees will learn to avoid questions, minimize exploration, and focus on immediate outputs. Conversely, leaders who model curiosity create environments where inquiry is rewarded. This does not mean celebrating randomness or distraction. It means demonstrating intellectual humility, asking better questions, and showing a willingness to challenge assumptions. One of the most powerful signals a leader can send is admitting what they do not know. This reduces the perceived cost of ignorance and encourages others to engage in learning. It also counteracts overconfidence, which is one of the main barriers to curiosity. Leaders can also design systems that embed curiosity into workflows. This includes allocating time for experimentation, encouraging cross-functional collaboration, and measuring not just outcomes but learning processes. Importantly, curiosity must be linked to performance. It is not about asking more questions for their own sake, but about asking better questions that lead to better decisions. In the age of AI, this becomes even more important. As machines take over routine cognitive tasks, the human advantage shifts to areas that require judgment, interpretation, and creativity. These are all downstream of curiosity. But judgment without experience is meaningless. AI can simulate answers, but it cannot substitute for the depth that comes from actually engaging with the world. There is a difference between consuming a microwaved meal and cooking one from scratch, sourcing ingredients, understanding how they interact, and adjusting along the way. The former is efficient and convenient; the latter builds intuition, tacit knowledge, and real expertise. In the same way, relying on AI-generated outputs without cultivating firsthand learning experiences produces a thin version of competence, what might be called artificial understanding. Curiosity, when acted upon, pushes us into those richer experiences that give judgment its substance and make our thinking genuinely our own. Curiosity as a strategic imperative The rise of AI has not just expanded access to information; it has quietly eroded the premium once attached to possessing it. When virtually all answers are instant, abundant, and convincingly packaged, the differentiator is no longer what you know, but how you engage with what can be known. In that sense, the economics of expertise are shifting. Knowledge, at least in its most accessible forms, is becoming commoditized, while the capacity to interrogate, refine, and build on that knowledge is becoming scarcer and more valuable. This is where curiosity earns its value, not as a soft or “nice to have” trait, but as the underlying mechanism that sustains learning over time. Without curiosity, the risk is not ignorance, but something more insidious: the illusion of understanding. AI can generate coherent explanations, summarize complexity, and produce plausible insights at scale. But unless these outputs are met with questioning, skepticism, and a desire to go beyond what is given, they are unlikely to translate into genuine insight or better decisions. The danger, then, is not that machines will think for us, but that we will gradually outsource the very effort required to think well, confusing fluency with depth and access with mastery. This places a different kind of demand on individuals and organizations. The task is no longer simply to adopt AI tools or increase their usage, but to integrate them in ways that augment rather than atrophy human judgment. At the individual level, this implies a degree of intentionality that is often underestimated: cultivating habits that prioritize inquiry over convenience, depth over speed, and exploration over closure. At the organizational level, it requires more than rhetoric about innovation. It calls for environments where questioning is not penalized by the pressures of efficiency, and where time spent exploring is not automatically seen as time wasted. And at the leadership level, it demands a visible commitment to curiosity as a norm, expressed less through slogans and more through behavior: the questions senior exeuctives ask, the uncertainty they tolerate, and the assumptions they are willing to revisit, will all shape the organization’s level of curiosity and appetite for learning. There is an obvious irony here. The more capable our machines become at producing answers, the more valuable it becomes to remain interested in the questions. This is not a nostalgic defense of human uniqueness, but a pragmatic recognition of where advantage now lies. In a world where everyone has access to the same tools, and AI becomes as ubiquitous as smartphones, Wi-Fi, or electricity, the differentiating factor shifts to how those tools are used, and that, in turn, depends on the quality of human curiosity brought to bear on them. Seen in this light, curiosity becomes a strategic necessity, one that shapes not only how individuals learn, but how organizations adapt and compete in an environment where knowing is easy, but understanding remains hard, and is quietly becoming a niche pursuit. View the full article

Understand the role Reddit plays in AI answers and how brands can capitalize on community signals to enhance visibility. The post Why LLMs Cite Reddit Instead Of Your Brand: A Practical AI Visibility Audit [Webinar] appeared first on Search Engine Journal. View the full article

In a world where wealth has always equaled accumulation, the future of wealth holds a counterintuitive opportunity. Boarding school… I didn’t expect that answer. As we sat chatting over a midday cappuccino, in a café filled with teak, surrounded by banana trees, and filled tropical ambiance, I’d asked a casual question as we discussed plans ... Read moreView the full article

As 7.4 million Americans sit unemployed, the path to employment has completely changed. Amid fake listings, AI filtering of candidates and widening talent pools, job seekers believe that they’re competing against a hiring ecosystem that penalizes honesty and rewards perception. The result? A hiring environment where the signals employers have traditionally relied on to evaluate candidates have become deeply unreliable. Now, both sides are operating with diminishing trust in each other. What’s Driving the Deception? Hiring today is not facing a character problem, but a structural one. When candidates believe that presenting themselves accurately will cost them a job offer, the rational response is to become the person they think the employer is looking for. But when this approach becomes standard, those who still choose to tell the truth take on an “honesty tax,” the systemic disadvantage honest candidates face when exaggeration becomes the market norm. GCheck’s Trust in Hiring Report revealed that 93% of job seekers have lied or embellished their experience during the hiring process, while 60% do not believe they would have been hired had they presented their qualifications more accurately. This is beyond a confession—it’s a market signal. Part of what drives this dynamic is opacity on the employer side. When candidates do not know what will be verified, they assume the answer is minimal, and they calibrate their self-presentation accordingly. In fact, GCheck found that although 88% of job seekers believe misrepresentation puts businesses at risk, 53% assumed employers wouldn’t verify their claims and only about a quarter (26%) report ever being caught lying or exaggerating. Verification that is invisible to candidates is not a deterrent. It is permission. And thanks to artificial intelligence, candidates can disguise their true skills and identity almost instantaneously. AI Accelerates Dishonesty in Hiring LinkedIn’s 2025 Work Change Report estimates that 70% of the skills used in most jobs will change by 2030, driven largely by AI. When job seekers navigate a market where the definition of “qualified” is constantly shifting, the pressure to appear more capable than they are significantly intensifies. AI has not created that pressure, but it has handed candidates sophisticated tools to act on it at every stage of the hiring process. Employer concerns have moved beyond job seekers’ using AI to compile resumes or assist with writing. Now, the degree to which AI has migrated into live interviews and assessments is worrisome. GCheck found that 61% of candidates have used AI to rehearse interview answers until they sounded more impressive than authentic, and 25% reported deploying an AI avatar in place of their own face during a virtual interview. ​ The result is a hiring process where trust is eroding on both sides. On one hand, candidates feel pressure to optimize and automate their performance in a highly mediated, virtual environment; on the other, employers struggle to assess who is genuinely behind the screen. When interviews are increasingly remote, scripted and technology driven, the lines between preparation and performance become blurred. This highlights how broken and transactional the modern hiring process has become. There’s also an emerging phenomenon of systematic embellishment, distortion or fabrication of professional qualifications across resumes, interviews, and references as a deliberate competitive strategy driven by market pressure and weak verification expectations. It’s been dubbed “careerfishing,” and it’s no longer the behavior of a fringe group. What Employers Must Do to Rebuild Trust Rebuilding trust in hiring is not only a technology problem, but also a standards and transparency issue. Employers who treat verification as a confidential back-end process get exactly what opacity produces: candidates who assume they can game the system, largely because they can. Three leadership-level shifts matter most here: Make verification standards visible. Communicate what will be checked before a candidate applies. Transparency disrupts embellishment at its source, not after the offer. The FTC’s guidance on employment background checks under the FCRA already mandates disclosure at specific stages. Moving that clarity upstream changes candidate behavior earlier in the process in measurable ways. For example, candidates who know credentials or work samples will be actually verified are less likely to exaggerate or rely on AI-generated materials they cannot defend later. Make screening decisions reviewable by a person. Candidates who know a human will review findings, not only an algorithm, engage with the process more honestly. Make verification proportionate to actual risk. Applying the same screening depth to every role signals to candidates that the process is performative. Calibrating scope to genuine role risk makes verification more credible, more defensible, and more likely to deter the embellishment it is meant to catch. In recent years, hiring integrity has evolved from a checkbox exercise into a strategic priority. When AI-driven careerfishing corrupts the foundational data a company uses to build its workforce, the damage surfaces in performance gaps. The goal is not to catch more people lying. The goal is to build a hiring environment where honesty carries a genuine advantage rather than a competitive penalty. When employers operate transparently and verify consistently, they stop performing diligence and start practicing it. That distinction is what separates organizations that attract trustworthy people from those that inadvertently select for the most convincing ones. View the full article

It’s five answers to five questions. Here we go… 1. I cry when people give me compliments So … the subject line kind of says it all. I cry when people give me compliments. Not a “you look nice today” kind of compliment, but the sincere, drawn-out, vulnerable kind. I always have and I’m not sure why, other than maybe I’m just a Very Emotional Person, although I’m generally even-keeled and not prone to emotional outbursts. I am a manager whose department is going through a reorganization, so I am switching teams and direct reports. I’ve worked with my current team for about three years and they are a wonderful group. I think I have very healthy boundaries, but also when you’ve been someone’s manager for three years, it’s hard not to develop some kind of attachment to them. People have shared very private and personal things with me, I’ve done what I can to support them through intensely difficult times, etc. The transition phase for this reorg was deliberately a bit lengthy, so it’s been a very drawn-out goodbye. At the last couple of team retros, some of the team members have talked about what I’ve meant to the team, how much they’ll miss me, the impact I’ve had, etc. I cried both times. Then I’ve been doing a round of last 1:1s and one of my employees really opened their heart up about how much they’ve enjoyed working with me. And I cried then too. I am glad to hear the feedback and happy to know that our working relationship was positive for them too. But I’m so mortified that I cry! And trust me, I do not have a pretty cry face (does anybody?). I wish I could just say, “Thank you, that means a lot” and get on with my life the way everyone else seems to. I’ve apologized for crying and told them I’m embarrassed, but they just say things like, “Don’t be embarrassed! It’s because you care so much that it’s emotional for you, and that level of care is what makes you such a great manager!” which has triggered more crying. I don’t want to tell them to not express their appreciation. So maybe I should talk to a therapist about why I cry at compliments? Or just embrace the sensitive and sentimental side of myself? Or try really hard to disassociate when people give me compliments? Aw, I think it’s okay that you’re crying in these situations. But you’re probably making it more awkward than it needs to be when you tell people you’re embarrassed by it, so I’d stop doing that. You can just say, “Thank you, that means a lot to me — as you can see!” I’d be more concerned if you were crying in other situations, like when someone disagrees with you or you get difficult feedback, because that can make people reluctant to have those conversations with you in the future and can make it seem like you can’t handle pretty routine parts of professional life. But crying because someone has moved you with an expression of appreciation is a different thing. I’m assuming you’re not, like, sobbing when this happens — if you are, then yes, that’s something worth talking with a therapist about. But getting a little teary? Totally fine in this context, and you might find it less embarrassing if you decide to just be matter-of-fact about it! 2. Was I wrong to say I’d miss a deadline if I was assigned more work? I work at a small web development company with about 20 employees. I’m a regular employee, not a manager or even a senior employee. We work 32 hours a week but are paid for 35. We’re supposed to have Fridays off, but we need to remain available for “emergencies.” I have mixed feelings about this: since we’re paid for 35 hours, it’s hard to complain if we end up working three hours on a Friday, but it also means we can’t plan anything for that day. From March through April, I was assigned to a stressful project with unrealistic deadlines. During those eight weeks, I worked at least 35 hours every week, and on two consecutive weeks I worked 40 hours. Any hours above 35 went into a time bank to be used later for appointments and such. I was doing my part to get the project delivered. During the final week of the project, my project manager asked during a daily stand-up how she could support me through the end of the project. I replied, “Everything is on track. The only thing I need is not to be assigned additional tasks or projects until the go-live.” I probably added a nervous laugh too. Two weeks after the project ended, I had my annual review with my manager, Fergus, who is also a developer. Fergus told me I shouldn’t have said that in the stand-up meeting. He said it was insensitive to say I didn’t want more tasks without knowing whether other employees were also working Fridays and overtime. I replied that I had answered honestly because that was genuinely what I needed, and that delivering the project successfully was my priority. He didn’t push the point further, and we moved on with the review. This has stuck with me. Should I not say what kind of support I need when my project manager asks directly? That feels completely backwards to me. I don’t really know whether my coworkers were also struggling with their workload, but it’s not my responsibility to monitor that. I help when asked and I step in during stand-ups when I can contribute. Was this just Fergus, tired of working Fridays himself, projecting on me because I tried to assert myself? Your wording seems fine to me. You weren’t saying, “You can’t under any circumstances assign me anything else.” You were saying, “Everything is on track as long as nothing additional gets added on to my plate; if it is, that would change my ability to make the go-live date.” Is Fergus generally someone who nitpicks wording or has rigid expectations about how people should communicate? If not, I’d chalk this up to him being stressed during a period of high workload, or just a miscommunication where he thought you were refusing to take on anything else, not just explaining how it would affect the first project. (Also, if you’re exempt, this pay set-up is legal, but if you’re non-exempt, they’re legally required to pay you for all the hours you work — so if you’re paid for 35 hours but work 40 hours, those hours need to be added to your paycheck, not banked for use later.) 3. A terrible singer in a volunteer choir I’m part of a volunteer choir. While we perform, it’s non-audition so there’s a real focus on having fun. It’s a lovely, fun experience with one exception: one singer in the tenor section sings very loudly and very off-key (in the “peel the enamel from your teeth” fashion), to the point where when he sings I’ve genuinely seen people wince or jump at the sound. It’s like being ambushed by a turkey bashing at a xylophone. He clearly knows that he sings loudly enough to bother other people but doesn’t particularly care about amending his behavior: he’s made jokes a few times to other people that he’s surprised he hasn’t driven them off with his volume. He also makes a point of coming to stand at the front of his section which – because of the layout in which we stand – means everyone is impacted; some of the alto/soprano parts have tactfully asked other tenors to try and get that seat so we’re not so impacted, but so far there have been no takers! (The impact of the sound is also enhanced by the fact that no one else in his voice part sings particularly loudly, so you only ever hear him.) I’m sure he’s a perfectly pleasant guy and it’s not his fault if he’s tone-deaf, but the effect this is starting to have on the rest of us (plus the fact that he’s clearly aware there is at least some issue but doesn’t try to correct it) is really having a negative impact. It’s incredibly distracting when we sing together and is starting to affect people’s enjoyment of the choir. Multiple people have said it bothers them, and some even some said they don’t want to come anymore because of it. We suspect that our choir leaders over the years have been aware of this problem, because the tenors have been having far more generic, “let’s try singing that part again” coaching since Turkey Guy joined our ranks. However no leader has seemed to pull him up on this directly, most likely because we’re non-audition and people are never pulled up on “errors” for that reason. Plenty of people sing rather imperfectly in our choir, but the off-key plus incredibly shrill volume is making this a double whammy. It’s impacting my enjoyment of the choir so much that I’m tempted to lay this out to our choir leader, say how much it bothers multiple people’s enjoyment of the group, and ask if it would be possible to suggest he tone the volume down. As this is a voluntary group though, I don’t know if there’s anything else I should take into account. Any advice? Yes, you can do that! If it’s driving you and others to consider leaving the group, the leader should know that. You’re not making a demand; you’re saying, “This is affecting the enjoyment I get from participating, and since I’m at the point of considering leaving over it, I wanted to bring it to you and see if anything can be done.” In general I’d try to avoid speaking for others — but if other people are telling you unprompted that they might leave over it, you’re allowed to reference that too, so the leader is aware it’s not just one excessively sensitive person. The leader might choose not to do anything about it, but it’s information they should have. And really, dealing with this kind of thing is their responsibility; if it’s the first time a potentially awkward conversation has come up, they’ve been lucky. 4. Should I tell my boss to fire our new hire? We just hired someone for my team who is, to put it lightly, not doing well. I work on a team of analysts who do a lot of technical writing for a niche industry. There are four levels, and he got hired at level three (so fairly advanced). But so far, he has: 1) failed to complete basic tasks on a reasonable timeline despite handholding from me, my boss, and another coworker, 2) provided work that is riddled with spelling and grammar errors and a lack of basic grasp of the technical concepts, and 3) often been unavailable during standard work hours and non-responsive to time-sensitive requests, 4) while exhibiting a real “I’ve got it, no worries” attitude. I’ve given him kind but firm feedback when he messes up things that we work on together, and I’ve also been making pretty pointed comments to my boss about my concerns about his performance. My coworkers have expressed similar frustrations/concerns. Should I straight up suggest to my boss that she should fire him? I’m worried about stepping over the line, but I’m also worried she won’t take action before his probationary period is over, and then we will be stuck with him (it’s very hard to fire people here once they’ve passed that mark). It’s not overstepping to tell your boss that, having worked with the new hire closely, you don’t think he’s able to do the job that your team needs done. For example, you could say: “I’m concerned that Bob isn’t able to do the work we need from his position, even with feedback, and that if he stays past his probationary period, it will cause real problems for the team.” You could add, “I’d love to say I’ve seen improvement or the potential to improve, but everything I’ve seen so far makes me think that’s unlikely.” 5. Do I need to apologize for my email address? I am an elder millennial born in 1988. I still use the email address I made up when I was 12 or 13. I have my birth year in my email. Let’s say it’s MyName88@fakename.com. Recently I found out that the number 88 has an anti-semitic meaning. Had I known or ever heard of this, I would never have put it in my email. My fear was that the “88” in my email will be seen as a dog whistle to certain people. To rectify this, I have made a new email address and am slowly transitioning over to it. But sometimes I forget to use my new one. I recently applied for a job using the old email address. Total accident — just an error in the slow email transition process. I made it through the first virtual interview and my next step is an in-person interview. Should I bring up the 88 in my email address during the in person interview? I’d prefer not to dwell on this, but I value integrity too much to let a suspicion like that go uncorrected. I’d rather squash it now so we can move past it. It’s extremely unlikely that anyone will think that; they’ll assume it’s your birth year or your graduation year or something like that (and I say this as a Jewish person). If you were giving off other signs that you were a giant asshole, then the “88” might be interpreted through that lens, but otherwise you’re fine and no one is likely to suspect you put the number there to let everyone know you hate Jews. You don’t need to bring it up (and shouldn’t). The post I cry when people give me compliments, a terrible singer in a volunteer choir, and more appeared first on Ask a Manager. View the full article

The outgoing chair has made some mistakes, but his decision to stand up to The President was heroicView the full article

Chinese territory enjoys surge of investment from mainland as wealthy spread assets across different jurisdictionsView the full article

Andrius Kubilius pushes for governments to open weapons stockpiles to Ukraine View the full article

Despite $17bn in pledges, organisation is stuck in limbo with no money flowing to projects in GazaView the full article

Sovereign borrowers look to issuance in US dollars, Swiss francs and other currenciesView the full article

The technology opens the door for smaller, well-funded challengers to take market share from the Big Four and others View the full article

Agreement with unions ends wrangling over how to share spoils of boom at memory-chip makerView the full article

iPullRank tested Gmail and Photos signals in opted-in AI Mode Personal Intelligence accounts. Gmail showed the strongest brand visibility lift. The post Gmail Content Linked To AI Mode Brand Visibility Lift appeared first on Search Engine Journal. View the full article

Albert Manifold’s behaviour and use of personal devices cited as factors in his removal over conduct and governance concernsView the full article

Hands-on approach was viewed as aggressive by several colleagues at UK oil majorView the full article

Foreign Office announces measures against Huobi among several entities it says helped Russia evade economic pressureView the full article

Last weekend, when Puck announced that the sustainable fashion startup Everlane had been acquired by the Chinese ultra fast fashion retailer Shein, it sent shockwaves throughout the fashion world. Michael Preysman, who founded Everlane in 2011, was just as shocked. “I found out the same time as everyone else,” he said in a LinkedIn post a week ago. “I’m not involved with the company anymore, and like many, am still digesting the news.” Well, Preysman is done digesting. And it seems that he’s ready to do something about it. Preysman just announced stillradical.com, a new venture that we know little about other than the bare bones website it launched with. The website lays out the new vision with brevity: “I started Everlane in 2011. Last week, the current management team sold it to Shein. So we’re starting over. Same principles, but a new take. And this time: no venture capital, no private equity.” The site says you can learn more by signing up for a waitlist. (Preysman did not respond to a request for comment.) Preysman launched Everlane when he was in his mid-20s, after starting his career in finance. His vision was to sell high quality products directly to customers online, without the markup of middlemen like department stores. This helped kickstart the direct-to-consumer movement that dominated the 2010s, producing brands like Away, Warby Parker, Allbirds, and Glossier. To fuel its growth, Everlane took an undisclosed amount of venture capital. A few years in, Preysman turned his attention to the human and environmental impact of the fashion industry. Everlane promised to eradicate virgin plastic from its supply chain, and showed customers inside the factories they used, to highlight how it was paying attention to the working conditions of laborers. All of this was good for business. By 2016, Everlane was valued at $250 million, although it was unclear whether it had ever become profitable. In recent years, its growth slowed. It went through two rounds of layoffs, once during the pandemic and then again in 2023. L. Catterton—the venture capital wing of the luxury conglomerate LVMH—bought a majority stake in Everlane in 2020. Shortly after, Preysman left the company to launch a new supplements brand called Magna in 2024. For many, Everlane’s acquisition by Shein was a disappointing final chapter for a company that stood for optimism and ethics. Clearly, Preysman felt the same way about it. This new business suggests that he hasn’t given up on the idea of sustainable fashion. However, he’s realized that venture capital is not the right tool for launching an apparel business. It will be fascinating to see what lessons Preysman has taken from the rise and fall of Everlane, and how we plans to build his new company differently. View the full article

Victory of flawed Senate candidate Ken Paxton could cement the president’s hold on the party View the full article

Current CEO Rick Thornberry is retiring as Radian shifts to a multi-line business, with former Mr. Cooper President Mike Weinbach taking over on Aug. 13. View the full article

Unlike some of his industry peers, OpenAI CEO Sam Altman has been surprisingly skeptical of the notion that AI is displacing workers. In an interview a few months ago, he argued that AI was a convenient scapegoat for some companies, echoing what some economists and experts have expressed about the narrative that AI is driving layoffs across corporate America. “I don’t know what the exact percentage is, but there’s some AI washing where people are blaming AI for layoffs that they would otherwise do. And then there’s some real displacement by AI of different kinds of jobs,” Altman said at the time. In an interview this week, however, Altman made a bolder statement, suggesting there was little evidence AI would do extensive damage to white-collar jobs, despite predictions to the contrary. “I’m delighted to ⁠be wrong about this,” he said on Tuesday during a virtual appearance at the Commonwealth Bank of Australia conference, according to a Reuters report. “I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than ​has actually happened.” “My intuitions were just off,” he added. “People are like, ‘oh, you could have saved the world a lot of fear mongering and a lot of doom and gloom.’ But at the time I was like, ‘I see this is a real risk. We should probably ​talk about it.’” Part of the reason for this realization, Altman claims, is that he underestimated the human element that so many jobs require. He had tried using AI to field emails and Slack chats, but increasingly found himself responding to those messages himself—which apparently led him to believe the impact on jobs will be different than he had originally anticipated. “I don’t think we’re going to have the kind ​of jobs apocalypse that some of the companies in our space advocate or talk about,” he said. While companies have repeatedly cited AI and automation when conducting layoffs, the labor market does not yet reflect a mass reduction in jobs across the workforce. On top of that, even as tech leaders remain bullish about the promise of AI, there are signs that all their spending may not yield the results they are expecting. In another recent interview, an Uber executive cast doubt on the idea that the company’s AI investments had meaningfully boosted productivity, despite blowing through its 2026 AI budget in just a few months. On the Rapid Response podcast, Uber president Andrew Macdonald claimed the growing use of Claude Code tokens had not necessarily resulted in better features for consumers. “That link is not there yet, right? I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25% more useful consumer features,’” he said. Still, that awareness may not help preserve jobs, especially as companies demand greater productivity from their workforce. Whether or not AI can replace workers, tech employers continue making cuts to headcount to offset their sweeping AI investments. Some workers are already feeling the effects of widespread AI adoption, from Amazon warehouse workers to people who hold administrative jobs—and despite the concerns about white-collar employees, researchers have found there could be significant downstream effects for workers without college degrees. For all his talk, even Altman has noted that there’s a chance the fallout from AI could be worse than it seems right now—and that it could eventually come for his job, too. View the full article

PR and SEO used to be separate disciplines. Now you can’t afford to keep them siloed. Google and LLMs both rely on third-party signals — backlinks, brand mentions, expert commentary, and coverage in trusted publications — to decide which brands deserve visibility. PR and SEO both generate those signals, but most teams still operate independently. When they do collaborate, it’s usually to treat PR as a link-building opportunity rather than a real partnership. This leaves authority on the table. But the real gains happen when these teams operate as one. In this article, you’ll learn a five-step playbook for turning PR and SEO into an always-on authority engine. I also spoke with two digital PR experts about how they’re partnering with SEO to build more authority across search, media, and LLMs. Free resource: Download our PR + SEO Outreach Planner to align pitching, prioritize outlets, and track results. It includes a pitch ownership guide for deciding who pitches what and when. Step 1: Align PR and SEO Research An always-on PR and SEO partnership starts with shared intelligence. Without it, you get predictable gaps: Content that ranks but doesn’t earn media mentions or AI citations Coverage that builds awareness but doesn’t improve search visibility AI citations and media coverage that go to competitors because they published first Use PR Insights to Identify Emerging Content Opportunities The biggest authority wins don’t come from PR and SEO staying in their own lanes. They come from each team sharing insights that shape angles, assets, and placements. For PR, this could be: A sudden spike in journalist inquiries or media coverage around a topic A new phrase or framing gaining traction among industry voices Recurring themes across newsletters, conferences, or trade publications Britt Klontz, digital PR consultant and founder of Vada Communications, says the strongest results come when PR and SEO combine their strengths at the ideation stage: The best collaborations with SEO happen when PR is brought in early, before an asset or campaign is completed. We used to ask, ‘Can PR promote this?’ Now we ask, ‘How do we build something together that will help with search, media, and brand visibility from the start?’ To facilitate this partnership, build a regular channel for PR to flag insights to SEO. This could be a shared Slack channel, spreadsheet, or standing agenda item. For example, when I was the editor of the Hootsuite Blog, our PR team notified us that LinkedIn was shutting down its “Elevate” feature and suggested we should write a blog post about it. No search volume existed yet, but we created the content anyway. The post started gaining backlinks and driving a surprising amount of demo requests almost immediately. Months later, the search volume appeared. And our post ranked #1. Today, it still ranks near the top of the SERPs for terms like “LinkedIn elevate alternatives.” AI tools like Claude also use the blog post as a top source for relevant prompts: That’s the power of PR and SEO sharing information and acting on it quickly. Rankings, backlinks, and AI citations that would have gone to a competitor built lasting authority for Hootsuite instead. Use SEO Insights to Inform Content Topics SEO has signals PR can act on too, including which topics are heating up and editorial gaps. When conducting keyword research for PR, SEO should flag two things: Informational gaps: Questions audiences are actively searching for, but no one is answering well yet Trending terms in your niche: Journalists are likely already interested, which gives PR a clear opening That’s why Rola Tfaili, communications manager for North America at Xero, brings SEO into her process from the start: I want SEO insights — like emerging search trends, keyword gaps, and audience intent — to directly shape our PR narratives and campaign angles from the outset, before content is developed. Here’s how you can do the same. Not all keyword tools show you trends over time, so I’ll use Semrush for this step. Note: If you don’t have a subscription, sign up for a free trial of Semrush One, which includes Semrush Pro and the AI Visibility Toolkit. Search any term in the Keyword Magic Tool and look at the “SERP Features” column. Two features in particular signal strong PR potential: News and Top Stories: Google surfaces these for time-sensitive or trending queries — sometimes within 24–72 hours of a news event. If your topic triggers these features, journalists are actively covering it, and PR has an immediate opening. Discussions and Forums: This signals that audiences are seeking advice or firsthand experience on the topic, which is often a sign of unmet demand and/or increasing interest Next, use the Keyword Overview tool’s 12-month trend graph to confirm whether a topic is gaining momentum, seasonal, or fading. A consistently rising trend is your strongest signal — media interest is likely to be building as well. Pro tip: Don’t overlook existing topics. A trending term you already own is a valuable opportunity. PR can pitch it to journalists as a timely angle, repurpose it into new formats, or use it as a hook for a broader campaign. For LLMs, you need a tool like Semrush’s AI Visibility Toolkit that shows actual prompt data, not just search queries. This gives you insight into the exact prompts your competitors are earning AI visibility for, but you aren’t. Those gaps are worth flagging to PR, especially if competitors are being cited as authorities on topics your brand should own. Use your shared doc or Slack channel to provide real-time insights, so neither team works from stale data. Topics that show up in both PR’s emerging trends and SEO’s keyword data are your highest-priority opportunities. Step 2: Collaborate on AI-Ready Assets An AI-ready asset is built to be found, cited, and trusted by search engines and AI models (while being valuable to humans). This is also called answer engine optimization (AEO), which is the process of creating and structuring content for AI systems. It can include optimizations like: Headings that mirror how people search Front-loaded key stats, details, and definitions Sections that focus on one core idea Bullet lists and tables that make key information more extractable When you combine PR’s distribution power with SEO’s technical expertise, you get assets that earn visibility across search, media, and LLMs. Original Research and Reports Original data has long helped brands earn backlinks — now, it helps you build AI visibility too. A collaborative workflow for this asset would look something like this: SEO identifies the topic based on search demand and content gaps, and PR validates whether the angle is pitchable and shapes the findings into quotable hooks. Together, they design the study so it’s structured for citations, with a clear methodology, front-loaded stats, and branded visuals that are easy to share. SEO content teams might be tempted to create this type of asset on their own, then ask PR to pitch it. But Britt says if PR is involved earlier, they can help answer questions like: Is this a real story? Is there a sharper edge here? Do we need more reliable data? Is there a better hook that fits the time? Would it be more interesting if an expert gave their opinion? That kind of information can make an asset more useful and impactful. Pro tip: Give your asset a unique, branded name — like ‘The State of X Report’ or ‘The X Index.’ If journalists mention it without linking, people can still search for it and find you. Don’t limit original data to a blog post. High-value assets should have their own crawlable landing page — no gates, no PDF-only content. Use the same URL each year for recurring assets to build authority. Then, link these pages to related content on your site (and vice versa). This way, search engines and AI see your topical coverage as connected, not random. Free Tools Free tools that solve a specific pain point earn AI visibility, backlinks, and return visits long after launch. This includes calculators, templates, checklists, and interactive assets. The gap here is usually distribution. SEO can build and optimize tools, but without PR’s contacts and timing, even the best ones can be limited by organic performance. A strong hook helps, too. Britt says an asset is easier to promote when it “blends search insights with something more personal, like proprietary data, a strong point of view, or a story angle that is relevant right now.” The payoff is an asset that is reported on and shared widely across channels. NerdWallet’s tariff calculator is a good example of this in action. It launched as tariffs dominated headlines — and earned media coverage because of it. Podcasts A branded podcast can generate tons of coverage, review articles, and inclusion in “best podcasts on X topic” listicles. Getting your experts on other podcasts is also valuable for building authority and visibility. Third-party mentions get your brand and subject matter experts into the conversation, both in search engines and LLMs. PR typically drives guest placements, but SEO can identify which shows already rank or get cited by AI for your target topics, so you’re pitching the ones that build the most authority. Press Releases When published on your site and optimized properly, press releases can become standalone, crawlable assets that increase your AI mentions. In fact, press release citations in LLMs grew 5x between July and December 2025, according to Muck Rack. To get the most out of press releases, both teams need to contribute. Rola has seen the benefit of this collaboration firsthand: For key assets like press releases, we integrate SEO insights early — before content is developed — and include SEO in the review process to ensure we’re maximizing visibility. PR shapes the story and the hook. SEO makes sure the on-site version is crawlable, optimized, backed by citable data, and linked to related assets. So the press release doesn’t just generate buzz, it feeds your broader authority. Explainer Content Explainers are easy-to-digest resources (usually articles or videos) that simplify complex topics or highlight key info about your brand. They help journalists and LLMs write accurately and consistently about you — especially if your category is niche or complex. SEO can use keyword and prompt data to identify the questions your explainers should answer and structure them so AI can parse and cite individual sections. PR knows which questions journalists and analysts ask most often — and where the current gaps are in how your brand gets described. The format can vary: One-page proof point packet with key stats and third-party validation that PR sends alongside pitches YouTube video with citable brand facts or product details Dedicated pressroom that organizes assets by category with founder bios and press releases (Bonus points for all three.) Step 3: Co-Build Your Third-Party Presence Brands are 6.5x more likely to appear in AI answers through third-party signals than their own content, according to AirOps. This means PR and SEO have a real opportunity to work together to build more visibility across search and LLMs. Rola sees this as an important shift for PR teams: When we align closely with SEO to ensure our key messages land in credible, third-party outlets, we’re not just generating press; we’re helping position the brand to appear in AI search platforms. That intersection between PR, SEO, and now AEO is where I think we’ll see the most measurable impact moving forward. Expert Commentary When your experts are quoted consistently — on your own site, social media, and in trusted publications — Google and LLMs begin to associate them (and your brand) with that topic. The biggest coordination gap is knowing where to focus. SEO has the data on which topics have the most search and AI demand — and which publications are already earning citations for them. PR knows which journalists and outlets are most receptive and what angles resonate. Together, they can pinpoint the exact publications and topics where a placement will improve results for both teams. Then shape the commentary accordingly. Concrete, data-backed quotes with a specific stat or firsthand insight are far more citable than generic thought leadership — especially for AI, which favors specificity it can extract and serve directly in an answer. Getting your experts quoted online is a strong start — but it works best when paired with the other authority-building sources below. Further reading: 5 Best HARO Alternatives (Expert Review) Review Sites and Forums Review sites like G2, Yelp, Google Reviews, and Trustpilot are trusted by AI for the same reason they’re trusted by humans. They aggregate specific, unbiased information about products from verified users. And AI frequently cites them for product recommendations: Reviews across multiple sites also strengthen your brand’s authority signals. It gives AI detailed evidence of what category you belong in, your core features and pricing, and why you should be trusted. Forums work similarly — AI pulls from Reddit threads and Quora answers when users ask for honest recommendations or firsthand experience. Brands that show up authentically and positively in these conversations earn another layer of trust signals. You can’t control these mentions, but consistently showing up as a helpful, knowledgeable voice in your category’s communities builds the kind of organic mentions AI models trust. PR and SEO should jointly identify which review sites and forums matter most in your industry. Keep review profiles current and monitor relevant forum conversations for opportunities to contribute genuinely. Further reading: How to Build a Brand Subreddit: Full Setup Guide (+ Examples) Wikipedia A Wikipedia page gives Google and AI a neutral, third-party source of facts about your brand. It also helps establish your brand as a recognized entity in Google’s Knowledge Graph. It’s a common source for Google’s Knowledge Graph, and it’s baked into LLM training data. But to qualify for a page, you need to meet Wikipedia’s Notability Criteria. This includes having significant coverage in reliable, independent sources that address your brand directly and in detail. PR can help you earn this kind of coverage by pitching stories about your company to journalists in reputable publications. Once you have a page, you won’t be allowed to edit it directly, as Wikipedia’s rules prevent self-promotion. But SEO can monitor the page for inaccuracies and flag corrections, and PR can handle reputation monitoring to keep the narrative positive. Pro tip: Use the same brand name, category language, and positioning everywhere: across your website, social profiles, press releases, and review site listings. The more consistent your language, the more confidently AI and Google can categorize and recommend your brand. Step 4: Unify Your Outreach Strategy If PR and SEO know what — and to whom — each team is pitching, you avoid mixed messages and misaligned timing. And your odds of a yes go up. It doesn’t take much to fix. Just a shared source list, a strategy to split pitching, and a regular check-in to stay aligned. Pro tip: Download our PR + SEO Outreach Planner to put the tips in this section into action. Build a Shared Target Source List SEO has a list of high-authority domains that show up in organic rankings and AI citations. PR has a list of journalists, analysts, creators, and publications that influence their category. Merging these gives you a single view of every third-party source worth going after. Build it as a shared spreadsheet with three columns: PR Sources SEO Sources AI Citation Sources Then prioritize. Any source that appears on more than one list goes to the top. It has double (or triple) the potential to impact your authority and visibility. Pro tip: Update your list quarterly as sources can shift fast — especially in LLMs. Create a shared pitch doc to go with your source list. Use PR’s standard pitch brief, or if one doesn’t exist, create one. Include headline stats, agreed-upon positioning language, and target URLs. Whoever sends the final pitch customizes it to their contact. But using the shared pitch doc as a starting point ensures your basic story stays consistent. Split Pitching by Strengths Many high-priority pitches will need both PR and SEO to weigh in. But not all. Divide the work of pitching based on what each team does best. Generally, that means structured, technical placements for SEO and editorial, relationship-based placements for PR. Your company may want to organize these tasks differently depending on industry or org structure, but here’s what I suggest: SEO PR Pitch for inclusion in industry listicles Pitch journalists and editors on newsworthy content Fix unlinked brand mentions Offer expert commentary to reporters Reach out to sites with broken or outdated links Submit to industry awards Identify warm contacts from referring domains Brief analysts at firms like Gartner and Forrester Monitor AI citations for new outreach targets Explore sponsored placements in newsletters, podcasts, and trade publications Plan Pitching in Advance Meet quarterly or monthly — whatever works for your schedules — to decide who is going to pitch what, to which outlets, and when. This will help prioritize high-impact efforts and reduce accidental duplication of work. Map outlets to objectives and target KPIs to determine ownership. Every time you meet, review results from the last period. Prioritize more of what’s working and cut what isn’t. Further reading: Journalist Outreach: 9 Steps to Earn High-Authority Links Step 5: Report on PR and SEO Performance Together PR and SEO usually track different metrics, like mentions and outlet quality vs. rankings and organic traffic. The fix isn’t merging into a single dashboard. It’s building a shared lens for evaluating what each asset actually did, no matter which team owns it. Britt recommends that both teams agree on a shared set of questions to evaluate each asset: Did it get any attention? Did it get picked up by reliable sources? Did it help with search goals? Did it contribute to conversions? Did it have results that lasted longer than a short-term spike? As Britt puts it: The best shared work usually helps with more than one thing at a time, like visibility, authority, discoverability, and brand credibility. Visibility: Did We Show Up in the Right Places? Getting in front of your audience more often — and in the places they care about — is one of the main advantages of having PR and SEO collaborate. Track these metrics to see if it’s working: Quality mentions in relevant outlets: Not raw mention count. A placement in a niche newsletter your buyers trust outweighs 10 mentions on unrelated blogs. PR likely already has a media monitoring tool for this. Recurring format mentions: Listicles, comparison posts, and “best of” roundups will continue to earn backlinks and AI citations over time. They also show how your brand is positioned relative to competitors. Track these separately in your media monitoring tool or a shared spreadsheet. Share of voice in category coverage: Report on the percentage of category coverage that mentions your brand vs. competitors. Free tools like Google Alerts and Mention’s share of voice calculator give you a general sense of how you’re doing. But paid media monitoring tools let you dig into specific platforms, outlet types, and topics. For AI specifically, track how often your brand appears in AI answers for queries you care about. You can manually check your top questions and prompts in LLMs to see if your brand is mentioned, but this gets tedious at scale. The AI Visibility Toolkit is helpful here. It automates tracking so you’re not manually checking every LLM for every query. You get an overall AI Visibility score for your brand, which measures how often you’re mentioned in AI systems compared to other brands. The Competitor Research tool shows how your AI visibility stacks up against competitors, which is one of the clearest ways to show leadership whether you’re gaining or losing ground. It also tracks your Share of Voice across AI platforms, a single metric that reflects the combined impact of your PR and SEO efforts. Authority: Did We Become More Credible? This is where you show if your brand is becoming a trusted source online. Start by tracking new referring domains. New backlinks matter too, but new domains are more meaningful because they represent more unique sources vouching for your brand. Reporting on your website authority is also helpful. This is a third-party estimate of the level of trust search engines are likely to assign to your domain, based on your backlink profile and other signals. Different SEO tools calculate it differently (and call it different things). So, focus less on the score and more on the direction it moves over time. Note: Meaningful changes to your Authority Score can take 3-6 months to appear. The AI Visibility Toolkit tracks your mentions, citations, and cited pages over time, and tells you percentage increases and decreases. When your authority score and AI mentions are both climbing, you’ll know your PR and SEO work is paying off. Expert commentary placements, direct requests from journalists, and new journalist relationships are also worth tracking. Increases in any of those areas are a strong signal that you’re gaining trust. Google Alerts can catch mentions to help you track expert commentary placements, but a tool like Semrush’s Brand Monitoring gives you a more comprehensive picture. It lets you track any query (SME names or other keywords) and provides: Total mentions Estimated reach Traffic Mentions with backlinks Sources (Social media, news, and blogs) Demand: Did It Help People Take the Next Step? Did improving visibility and authority have any impact on your business goals and revenue? PR and SEO sometimes sit at the top of the funnel, so this can be tricky to answer. Start with these metrics to prove demand: Referral traffic Assisted conversions Branded search lift Track your referral traffic to show the number of visitors who visit your site directly from media coverage. Even if numbers are low, they’ll tell you which topics make your audience want to know more about you. Then you can publish more on those in the future. Tracking assisted conversions shows you conversions where organic search or referral traffic appeared somewhere in the buyer’s journey, but not necessarily as the last click. PR and SEO content may not convert on the first visit, but it still influences the buyer’s journey. This metric captures that concept. Find this in GA4 under Advertising > Key event attribution paths, and switch to “Source/Medium” to see which specific outlets have the most impact. As AI search has decreased click-through rates, branded search queries have become one of the clearest signals that your PR and SEO efforts are building real awareness. It’s a metric Britt prioritizes for exactly this reason: I track branded search lift because it’s a sign that coverage or visibility made someone curious enough to go look up the company by name. That matters to me because not every asset will result in direct clicks. The metric is also important to Rola: Branded search lift connects awareness and intent, showing how media exposure actually drives people to seek out your brand. Google Search Console tells you how often people search for your brand by name and how many of those searches result in a click to your site. Look for spikes around major coverage dates to directly tie increases to your PR and SEO efforts. Turn PR and SEO Into an Always-On Authority Engine The brands earning the most trust right now aren’t doing it with PR or SEO in siloes. They’re showing up consistently across media, blogs, review sites, search engines, and AI because all of those channels feed the same authority signals. That takes more than a “quick sync” before campaigns. It takes an always-on partnership. You don’t need to overhaul everything at once. Start small: Co-create one high-impact asset (and keep AEO best practices in mind) Merge your source lists Plan 3 pitches using our PR and SEO Joint Outreach Strategy Template When you’re ready to go deeper on how to optimize your brand’s presence in AI, check out our complete guide to AI optimization. The post PR and SEO: How to Build More Authority Together (5 Steps) appeared first on Backlinko. View the full article

Tehran vows to ‘not leave any mischief unanswered’ after the attacks View the full article

Most enterprise generative AI investments have yet to deliver the value companies envisioned, and every day, more leaders are recognizing that people lie at the heart of the struggle. In this year’s AI & Data Leadership Executive Benchmark Survey, 93% of executives leading AI and data efforts identified human issues around culture and change management as the primary obstacle to adoption. McKinsey Global Managing Partner Bob Sternfels put it plainly on HBR’s IdeaCast: “Half if not more of the secret sauce” in getting value from AI, he said, “is organizational change, as opposed to technology implementation.” As such, many leading companies have launched initiatives over the past several months to drive AI adoption across their workforces. These efforts run the gamut from carrot-to-stick approaches, with some rolling out hackathon programs and prizes for innovative uses. Others use weekly logins and token consumption as proxies for performance. A PERFORMATIVE APPROACH Leaders are right to focus on the people side of adoption. They need to be deliberate, however, about what they’re encouraging. I’ve learned something in my three decades helping some of the world’s largest companies through culture transformation. Employees prioritize what leaders model, incentivize, and reward. And initiatives built around shallow metrics can do more harm than good. It’s understandable why many leaders today celebrate deliverables simply because they were made with AI, or reward employees for integrating it into workflows. Facing underwhelming internal adoption metrics, many have come to see any increase in AI usage as a win. At my firm, however, we call this “trophy-style” AI adoption—which is to say, a performative approach focused more on usage than results. It’s focused on participation trophies over proof of impact. Leaders need to be wary of this trap. Because as anyone following the “workslop” problem or the emerging research on cognitive atrophy will know, not all AI use cases are created equal. Trophy-style adoption creates a dangerous illusion of progress, where activity masquerades as impact. In other words, we’re rewarding output over outcomes. A culture built around shallow adoption risks more than struggling to achieve ROI; in some cases, it might leave employees less equipped to meet business needs than prior to AI. IMPACTFUL ADOPTION Impactful AI adoption will look different based on the company, a person’s role, and many other factors. For some, it means deepening the quality of the same work product. For others, it means increasing output without sacrificing quality. And for still others, it means getting the same work done in less time, repurposing time and energy toward new questions and tasks. All the best adoption initiatives, however, will be reverse-engineered from the larger business strategy. They will be built around metrics that connect to it. The process of designing an adoption initiative should start with clarity and specificity around big-picture questions. What does value look like for our organization? How can different roles change to better deliver it? Leaders cannot lose sight of these framing questions as they determine what gets modeled and encouraged. Wise ones will drive for real business impacts that come from the usage. And when they showcase strong use cases, they will not just reward speed or deep integration. Instead, they will keep the focus on the larger picture, taking great care to explain the meaningful organizational outcomes driven by the use case. In Gagen MacDonald’s latest white paper, we dive into what it takes to do this well, and what organizations can do to bridge the separate realities that exist between leaders and employees around AI. Because while the employees who create the most impact with AI will certainly use it frequently, it’s a mistake to think of usage as synonymous with impact. And given how much companies have spent and plan to keep spending on this technology, it’s not a mistake many leaders can afford to make. Maril MacDonald is founder and CEO of Gagen MacDonald. View the full article

A day after Pope Leo XIV released his much anticipated encyclical on AI, the response from the AI community has been mainly positive, with some quibbling over the nature and potential of the technology. The Vatican released the encyclical, titled Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence, in Vatican City on Monday. (An encyclical is a high-importance teaching document issued by the Pope.) Broadly, the document aims to demarcate the fundamental differences between humans and machines and warn about the dangers of allowing AI to be controlled and distributed by a small group of people. The Pope denounced the “culture of power” driving the AI race, referencing the small set of wealthy investors and tech companies currently controlling the development and distribution of the technology. Pope Leo’s concerns also reflect a much broader religious debate around AI that has emerged across faith traditions in recent years. Religious leaders and scholars from Christianity, Judaism, Islam, and Buddhism have wrestled with everything from AI-written sermons and chatbot theologians to the technology’s impact on labor, misinformation, warfare, and the environment. While some communities have embraced AI tools for research, translation, and religious education, many leaders have emphasized that machines cannot replace divine inspiration, moral judgment, or the human relationship to faith. Pope Leo himself recently warned priests against using AI to prepare homilies, arguing that artificial intelligence “will never be able to share faith.” Within the AI research community, the response to Magnifica Humanitas has been mainly positive. One researcher, Chris Olah, participated in the Vatican’s process of shaping the ideas behind the encyclical. “This clearly raises questions beyond computer science,” Olah said in a companion statement to Magnifica Humanitas. “The machinery that makes this possible is the work of math and programming and science. But what character we choose, how it interacts with the world, how it ought to interact with the world—these are more clearly questions for the humanities, for religion, for philosophy, for society at large.” The encyclical, which is 85 pages long, was informed by extensive conversations with scientists, engineers, educators, political leaders, and families, the Vatican said. ‘The Pope is right’ Author and systems architect Daniel Jefferies broadly agreed on X with the Pope’s warning that AI will reflect the values of the people and institutions controlling it. “The Pope is right: AI takes on the characteristics of those who build it, finance it, and regulate it,” Jefferies wrote, before arguing that concentrated corporate control over AI could create “digital oligarchies” resembling modern-day East India Companies. AI pioneer and Turing Award winner Yann LeCun retweeted Jefferies’ post. Another Turing Award winner, Yoshua Bengio, echoed the Pope’s concern over AI’s potentially destructive power. “Like nuclear energy, AI must be at the service of all and of the common good. Decisions about technology must never be separated from conscience and responsibility.” ‘Relatively mundane AI dangers and changes’ Not everyone in the AI community agrees with the Vatican’s view of what AI is, or what it could eventually become. The encyclical draws a firm distinction between AI systems and human beings, arguing that machines cannot possess consciousness, morality, or lived experience. “So-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships and do not know from within what love, work, friendship or responsibility mean,” the document states. AI commentator Zvi Mowshowitz took issue with that premise.“ The central claim, wherein Pope Leo denies that AIs can think or importantly be minds, is wrong, as Olah points out in his statements,” he wrote on Substack. “Without the understanding of what AI is capable of becoming, the document effectively only deals with relatively mundane AI dangers and changes, although that on its own is still rather quite a lot to deal with and discuss.” AI policy analyst and writer Dean Ball says the church should focus on helping humans flourish as AI evolves further. “Some think I want the Pope to ‘ensoul’ AI or acknowledge AI feelings. I don’t,” Ball posted on X. “What I want is for the Church to contemplate what *humans* should do as we are eclipsed as the smartest entities on the planet, at least for many reasonable people’s definitions of the word ‘smart.’” President Donald The President has yet to post about the encyclical on Truth Social, but his former AI “czar,” David Sacks, weighed in. Sacks isn’t worried about letting a small set of AI companies effectively regulate themselves; he’s worried about giving the government the power to do it. (And who can pass up a couple of literary and historical knowledge flexes when the opportunity presents? Not Sacks.) Writing on X, David Sacks argued that giving governments broad authority over AI in the name of safety could ultimately enable censorship, surveillance, and social control. Invoking both George Orwell and the Latin phrase “Quis custodiet ipsos custodes” (translation: “Who will guard the guardians?”), Sacks wrote that “The oldest questions of human nature and authority don’t disappear in the AI age. They become newly relevant.” (In response to Sacks’ X post, a number of prominent voices in the AI world have since weighed in. Among them was Hugging Face CEO Clem Delangue, who argued that “the most important AI risk is concentration,” and cognitive scientist Gary Marcus, who warned against “leaving unelected private companies the ability to censor, surveil and control citizens.”) The The President administration, influenced by tech nouveau right figures like Sacks and Marc Andreessen, has gone to great lengths to keep the AI industry free of government oversight and regulation. Notably, most of the tech nouveau right crowd has remained silent on the Pope’s treatise. There have been no public comments yet from Elon Musk, Andreessen, Palmer Luckey, Balaji Srinivasan, Keith Rabois, Joe Lonsdale, or Jason Calacanis. View the full article