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YouTube Shorts, the shortform platform from Google-owned video giant YouTube, has seen massive success since its launch in September 2020. Today, an estimated 1% of all waking human hours are spent watching Shorts, with videos amassing around 200 billion views daily. But what users watch is ultimately shaped by YouTubes algorithmand a new study published in the Cornell University preprint server arXiv suggests that the algorithm nudges viewers away from politically sensitive content. When you start [watching] a political topic or specific political topics, YouTube is trying to push you away to more entertainment videos, more funny videos, especially in YouTube Shorts, says Mert Can Cakmak, a researcher at the University of Arkansas, Little Rock, and one of the studys authors. Cakmak and his colleagues scraped between 2,100 and 2,800 initial videos across three themes: the South China Sea dispute, Taiwans 2024 election, and a broader general category. They then followed 50 successive recommendations for each video under three viewing scenarios, which varied how long a simulated user watched: 3 seconds, 15 seconds, or the full video. The researchers tracked how YouTube presented 685,842 Shorts videos. Titles and transcripts were classified by topic, relevance, and emotional tone using OpenAIs GPT-4o model. When engagement began with politically sensitive themes like the South China Sea or Taiwans 2024 election, the algorithm quickly steered users toward more entertainment-focused content. The emotional tone, as assessed by AI, also shiftedmoving from neutral or angry to mostly joyful or neutral. Early in the recommendation chain, videos with the highest view counts, likes, and comments were favored, reinforcing a popularity bias. Maybe some people were aware of this, but Im sure the majority of people are not aware what the algorithm is doing, Cakmak says. They are just going and watching. Neither YouTube nor its parent company, Google, responded to Fast Companys request for comment on the studys findings. Cakmak doesnt believe this is a deliberate effort to suppress political discourse, but rather a design choice focused on user engagement. What YouTube is trying to do, he says, [is] remove you from that area or topic, and push you [to a happier] topic so that it can increase . . . engagement [and] earn more money.
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At the conclusion of an interview, its customary for the hiring manager to ask the job candidate if they have any questions. You’ve probably responded with generic questions like When will I hear back? or What are the next steps?” But there are more compelling questions about the position you can ask before the end of the interviewto help you and help you stand out, leaving a positive lasting impression and reiterating your interest in the job. Why are compelling questions important at the end of an interview? When job candidates ask thoughtful questions, it sends a powerful message to employers. It shows that they’re not just invested in their own success but also in the well-being and goals of the organization, says Jenny Matthew, director of talent acquisition and attraction at Paycom Software, who is based in Oklahoma City, OK. It also gives the candidate the chance to clarify any gray areas they may have about company culture, expectations of the position, and next steps. These questions demonstrate youre doing your due diligence to determine whether the role is a mutual fit for both the organization and you, she says. The last few minutes of an interview can leave the interviewer with a lasting impression The conclusion of your interview is a final chance to offer parting thoughts or even bring a new idea to the table for the interviewer to ponder after the interview. The end of the interview is the mic-drop moment, says Matthew. It allows job candidates to be intentional by reiterating who they are, what they bring to the table, their interest in the position and company, and some of their future desires. 3 compelling questions for the conclusion of your interview What are some gaps in the organization you are hoping I can fill? This question encourages the interviewer to reflect on the candidates credentials. The hiring manager can then delve into how your skills and experience can be a win for their team. The conversation also affords them the opportunity to explain how they would manage some specific projects, says Matthew. This interview question also demonstrates that youre genuinely interested in the companys priorities and goals, and eager to contribute to its success, she notes. How would you describe the company culture? Beyond the job description, an eagerness and curiosity to learn about a companys internal culture shows interest and professionalism. This question highlights a desire to learn about the inner workings of the company, says Matthews. Also, this question can be used to the candidates personal advantage as they are navigating their job search. This question gives the candidate insight into potential red flags and/or reasons why a certain role has been open for a long period of time, Matthew says. Employers are not the only ones in the drivers seat; the job candidate is conducting an interview of their own. By asking informed questions at the end of the interview, candidates demonstrate that they’re not just looking for any job, but a meaningful opportunity that aligns with their goals and work style. What does success look like in this role, especially within the first 90 days? After learning about the scope of the role, the corporate culture and how your skillset could be an asset, inquiring about expectations of the position is beneficial. This helps candidates ensure theyre aligned with the hiring manager on job responsibilities and expectations, while also understanding by what metrics job performance is assessed, explains Michelle Reisdorf, a district director at Robert Half in Chicago. It also demonstrates to the hiring manager a commitment to starting strong and succeeding early. Should you go off script at the end of the interview? Applicants should ask relevant questions that stem from the conversation, sticking to what was discussed, the position, or something on point to the company. Going off script is encouraged so long as the questions clearly reflect active listening and thoughtful consideration of whats been discussed, says Reisdorf. This approach can often demonstrate genuine engagement and curiosity about the role, as well as strong communication skills.
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Sam Altman recently observed how different generations are interfacing with AI: Older people use ChatGPT like Google. People in their 20s and 30s use it as a life advisor. [College students] use it like an operating system. What we share across ages is a fascination with this technology. But the vast disparity in use casesboth among generations and individualsled me to wonder about the distinctions among the AI models themselves. To parse them out, I let the AI models speak for themselves. I asked each to identify their own strengths and weaknessesas well as those of their competitorsthen weigh in on which was most likely to lead, which was most likely to go haywire, which was most useful today, and which ones I had overlooked. Then I took it a step further, inviting the LLMs to critique the survey results themselves: Which gave the best and worst answers? Which did the best job representing its own platformand which missed the mark? Each LLM also provided a self-assessment, and finally, had the chance to rebut criticism, pose questions to its peers, and respond in kind. Before you spend $20, $200, or more a month, you need to know which generative AI model you actually need. Now you can hear it from the models themselves. (Note: this exercise was conducted with Grok 3, weeks before its fascist meltdown.) The LLM vibe divide With few exceptions (Grok being Grok), the LLMs responded with striking self-awarenessadmitting flaws, hedging praise, and expressing a desire to improve. Nearly every model, most notably ChatGPT, cited hallucinations as their Achilles heel, reaching consensus on the need for better grounding and real-time accuracy. In assessing themselves and their peers, however, they tended to focus more on personality and tone than any hard performance metrics, the kinds of stylistic differences that reflect many of the current tensions between safety and innovation throughout the AI space. Grok took heat for its personality, Claude for its caution, and nearly all weighed in on how to strike the right balance between the two. On Team Safety, Claude is the clear captainthe designated driver of the LLM crew. Nearly all of them cited as its biggest strength its emphasis on safety and alignment, reducing harmful or biased outputs (in Claudes own words), with critiques pointing more to an excess of caution than any technical failings. Still, even Claude acknowledged the potential downside: If my safety orientation prevents me from being as useful as I could be, thats something worth addressing. At the other end, the Most Likely to Go Haywire superlative consistently went to Grok, with LLMs sharing concerns that its quirks might undermine its cred. If Claude is filling up water glasses for its friends at the bar, Grok is getting shotsor possibly starting a brawl (clapping back to ChatGPT at one point: lets not pretend youre flawless pal). Between barbs, however, Groks attempt at having a conscience emerged. The perception of bias tied to xAI or Elon Musk stings, Grok said, noting that it undermines my goal of being a broadly reliable, truth-focused AI. {"blockType":"immersive-block-embed","data":{"embedSource":"","embedImageDesktop":"","embedImageDesktopCaption":"","embedImageMobile":"","embedImageMobileCaption":"","backgroundColor":"","paddingTop":0,"paddingBottom":0,"paddingLeft":0,"paddingRight":0,"mediaType":"ceros"}} The AI Generalists The LLMs tended to agree that versatility is their chief KPI, whether they are already thriving in this capacity (ChatGPT, Claude, Gemini) or not (Grok, DeepSeek). ChatGPT was widely recognized as the most versatile player on the field. Balancing reason, creativity, and conversation to universal acclaim, it was the consensus pick for both Most Useful to Me Right Now and Most Likely to Rule Them All. Being a generalist trades depth for breadth, ChatGPT said. I may not outperform a specialist in narrow domains, but I aim to offer consistent, high-quality help across diverse tasks. Other models, which optimized for specific domains (Grok for culture, Copilot for enterprise, DeepSeek for coding), were praised within their lanes but penalized for general-purpose limitations. Models deeply integrated into existing platforms (Gemini with Google, Copilot with Microsoft, Grok with X) were perceived as capable within their ecosystems but constrained beyond them. And while open-source AI models like Llama and DeepSeek received kudos for their transparency, they drew criticism for their reliance on customization, viewed more as developer tools than end-user solutions. The AI specialists Fast Company has reported that Googles new search will change the way we browse the internet. Gemini seems built to usher that change forward. Great for fact-finding, less for banter, Gemini cuts to the chase with real-time, sourced information. Perhaps the best display of its personality comes in an explanation of how it stays so even-keeled: I maintain consistency in reasoning within large context windows by employing advanced attention mechanisms that effectively identify and weigh relevant information across . . . Okay, Google. If Gemini is the new Google, Copilot is the new Microsoft. Do you love using Microsoft products? Hate them? Use them begrudgingly for work? This will map closely to your experience with Copilot. ChatGPT championed Copilot as unmatched for enterprise productivity tasks, but agreed with its peers that it was largely inert outside that context. As DeepSeek succinctly put it: limited personality and heavily tied to Microsoft products. And then theres Llama, which we can nly hope is not the new Meta. Open-source, but at what cost? Llama struggled with the survey itselfoffering vague or confused answers, and ultimately looping on repeat responses. Three of the seven LLMs rated Llama as the Worst Response. In its own words: [My] open-source nature can make it challenging to ensure consistency and quality across different implementations. Far more coherent in the open-source field (and far more enjoyable to work with) was DeepSeek. Though all LLMs (itself included) agreed that coding is DeepSeeks core strength, it also presented a spirited personality throughout the survey process, its humble rebuttals always closing with a friendly jab at its accuser. In DeepSeeks words, This is why LLM peer review > human feedback. Were petty but efficient. The rumble During the initial survey (when they shared their elevator pitches, strengths, weaknesses), the AI platforms were objective in tone, with most saying the same things about themselves and each other in different words. But when I convened them for a discussion of the surveys results, their personalities (or lack thereof) came out in full force. When ChatGPT accused Grok of coming off vague or self promotional rather than informative, going so far as to say it read more like a hype deck, Grok took it personally. Ouch, ChatGPT, going for the jugular with hype deck? . . . Sounds like youre projecting a bitworried Im stealing your versatile thunder? Fending off its other critics, Grok claimed that Llama was sitting on the fence so hard its gotta hurt and that DeepSeek was swinging hard but missing the target. Then Grok extended an olive branch to DeepSeek: Youre not wrong about Llamas vagueness, thoughnice to know we agree on something. DeepSeek took a lighter approach, copping to its errors, dropping winks of sarcasm, and ultimately seeking truce. When CoPilot called DeepSeek out for reducing it to Microsoft dependency, DeepSeek volleyed back, My badyoure a beast in Office-verse. Now roast my Chinese NLP quirks and were even. Llama was predictably disappointing in its sheer indifference to the whole affair (its possible that our priorities in response style and content differed), and Claude was predictably reassuring in its thoughtful balance of concessions, pushbacks, and pivots to the deeper issues behind the critique. The debrief I then invited the AI platforms to shake it off and engage in a more civil dialogue, giving each model the opportunity to bring their burning questions to their peers, hear their answers, and offer a final word. Posing 30 questions in all, the LLMs were selective in who they queried. Gemini, ever fact-finding, was the only LLM to have questions for all of its peers, while Grok (even less surprisingly) was the only one grilled by the full panel. Claude, Copilot, and DeepSeek drew the least attention, receiving only three or four questions from the group. Some AI models doubled down on their personas, like Grok calling its ability to balance real-time wit with factual accuracy a powerful combo. Others engaged in a quiet brand repair, with Claude reframing caution as creative trust: When users know I wont go off the rails, theyre more willing to explore interesting ideas with me. And ChatGPT showed unexpected vulnerability when confronted about its default status, admitting the label can make people treat me like a search engine or a novelty. The dialogue revealed that these systems are grappling not just with technical limitations, but with identity, and how they want to be perceived by the humans they serve. The question may not be which AI will win, but which well want to live with.
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