You’ll discover how the shift in news revenue models—from public service to profit-driven, much like social media—is a core reason we're seeing so many echo chambers today. It's highlighted that news wasn't always expected to be a profit center, which offers a striking contrast to how current news outlets operate. You’ll understand that these outlets are now incentivized to keep your attention by showing you content that aligns with your existing beliefs, reinforcing those cognitive biases and creating your personal 'bubble'. This clip reveals how news organizations effectively 'own' a segment of the audience by constantly feeding them specific narratives, leading to vastly different perceptions of reality depending on where you get your news. You’ll see how social media is explicitly called an 'outrage engine,' primarily designed to keep you engaged by triggering strong emotional responses. It challenges the common assumption that platforms only show you what you agree with, pointing out that content you vehemently disagree with can also significantly boost your engagement and time spent. You’ll realize how these sophisticated algorithms leverage both agreement and disagreement to maximize your attention, creating a powerful and often addictive feedback loop. The revenue models of news outlets have gone to similar to social media advertising and how much time can we retain you on the channel or on the on the app or on the website. Commodity is your attention. social media is as as you know it's it's a it's the most intense form of those uh reinforced algorithms outrage. it's an outrage engine. What is identified as one of the main reasons for the current state of news media, including filter bubbles and echo chambers? According to the speaker, what has become the primary 'commodity' for news outlets in their new revenue model? How do modern news outlets often ensure user retention and engagement, according to the transcript? What was the traditional expectation for TV news in terms of revenue generation, as described in the past? What term is used to describe social media's intense amplification of reinforced algorithms, particularly through strong emotions? You'll hear how science is often 'weaponized' on social media, where people use snippets out of context to make their arguments seem more authentic, playing into that idea that 'every good lie has a grain of truth.' You'll discover how platforms are tackling this by linking news stories directly to the full scientific reports and even summarizing them for you, ensuring you get the complete picture rather than just a misleading headline. The conversation explores how AI can actually be a powerful tool for improving your news comprehension, helping you sort through hundreds of article versions and highlighting what different outlets agree or disagree on, all without adding biased judgment. You'll dive into the dual nature of AI's impact on journalism, from the concerns about deepfakes and synthetic headlines distorting reality to its potential to actually help identify bias and improve content quality, even highlighting how bias often hides in adjectives. You’ll hear a really thought-provoking take on how the increasing sophistication of AI in creating deepfakes could actually lead to a complete breakdown of trust online. Imagine a future where even the people who currently believe fake news stop believing anything on the internet because they're sure it's all faked – that's the powerful concern you'll explore here. The clip makes you ponder a world where we might abandon the internet as an objective information source, potentially sending us back to old-school methods like reading books or just talking to people face-to-face in the town square. science is weaponized because it's it's it adds um heft to an argument. it could signal the end of the internet when deep fakes become so good and it's known that they're good that all the people who used to believe the fake news won't believe the fake news anymore because they'll be sure that it was faked. According to the speaker, why do people often use scientific excerpts in social media arguments, even if out of context? How does Ground News utilize AI to help users navigate complex news stories and studies? Where is bias most often found in news reporting, according to the speaker, particularly when discussing AI's role in identifying it? What is Neil's primary concern regarding the widespread proliferation of highly realistic deepfakes? What is the general consensus regarding AI's potential impact on news and information, as discussed in the segment? You'll hear how different news sources can make us feel like we're living in completely separate realities, depending on what we read. This clip explains how news isn't just reported but fractured by things like a source's bias, funding, or even its target audience. You'll discover a unique approach that doesn't tell you what's right or wrong, but instead shows you the full spectrum of how a story is covered from left to right. It really highlights how much they trust your ability to think critically and piece together the full picture yourself. You'll learn that they don't actually decide if a news outlet is left or right themselves, which is pretty clever for staying neutral. They pull from three different kinds of bias rating services – think crowdsourcing, expert analysis, and even AI – to give you a really well-rounded picture. This segment reveals a super important point: sometimes, the biggest sign of bias isn't what a news source says, but rather what it completely chooses to ignore. You'll hear how some outlets use a 'nothing to see here' approach, which subtly shapes your perception without you even realizing it. how we view news is that something happens as you call the objective truth. something happens and then it goes through this prism of the media landscape and then it fragments into all these million of different versions of what what exactly happens It's the lack of coverage completely that that tells the bias of the out. What is the primary goal of Ground News in addressing the 'news problem'? According to the speaker, how does the 'objective truth' transform within the media landscape? How does Ground News determine an outlet's bias (left, center, or right)? Besides the framing and topics covered, what unexpected factor is identified as a significant indicator of a news outlet's bias? What is Ground News's approach to presenting different news versions to its users?