Traditional Media, Social Media and Chatbots All Act as Gatekeepers, But Chatbots are Most User-Driven

As a graduate student in a journalism program I assisted on a study of journalism education. It was pre-internet, so that did not emerge as one of the conclusions. We did not anticipate social media, nor did we imagine chatbots also would emerge. 

But the idea that news media shape the agenda of what people think about is one of the most established concepts in communication research, and so it makes sense that chatbots might emerge in that role as well. 

The central argument is that the media do not necessarily tell people what opinion to hold, but they have considerable influence over which issues people regard as important, which issues receive attention, and which subjects dominate public discussion. Social media has emerged in that role as well.  

AI chatbots may extend it further by synthesizing information and presenting a seemingly-comprehensive explanation of the issues. In other words, it is possible that chatbots shift the terrain from telling people what is happening to influencing what people think is happening and why it matters.

As with seemingly all media these days, the agenda-setting shifts from “what is happening” to something more akin to “what it means,” on an increasingly-personalized basis. 

To be sure, chatbots do not yet have the reach of social or traditional media. A 2025 survey by the Pew Research Center found that 75 percent of respondents say they “never” get news from chatbots. In 2026, Pew finds few Americans asking chatbots for election information. 

  

source: Pew Research

Still, the shift of agenda setting is clear enough:

• Traditional media agenda set by editors and journalists

• Social media agenda set by algorithms

• AI chatbots (an extension of search) set the agenda. 

Traditional news media, social media, and AI chatbots all exercise agenda-setting power: they influence which issues receive attention, which aspects of those issues stand out, and which issues people associate with one another. 

The main difference is how they exercise that power: news organizations select and emphasize stories; social platforms distribute attention through networks and ranking systems; chatbots synthesize information into answers shaped partly by the user’s questions. Of the three platforms, chatbots are the most user-driven. 

Dimension	Traditional news media	Social media	AI chatbots
Main mechanism	Editorial selection, prominence, and repetition	Network circulation, recommendations, and algorithmic ranking	Retrieval, selection, synthesis, and ordering within an answer
Principal gatekeepers	Reporters, editors, owners, and institutional routines	Platform designers, ranking systems, creators, and users	Model developers, training and retrieval systems, behavioral rules, and user prompts
What receives prominence	Stories judged newsworthy	Content made visible through platform and network processes	Topics, explanations, and sources selected for the response
Audience’s role	Choose outlets and respond to coverage	Publish, follow, share, comment, and influence circulation	Ask questions, specify priorities, and revise answers through follow-up prompts
Typical agenda structure	A relatively shared editorial agenda within an outlet	Multiple overlapping, personalized streams	A potentially individualized agenda constructed in conversation

When using traditional news, the audience generally chooses among editorial products: an outlet, program, section, or story. But it is a top-down process, in any case. 

With social media, users help shape their information environment through follows, searches, posts, and interactions, but still encounter streams of material selected and ranked by others. 

With a chatbot, the user can commission a response whose subject and organization need not correspond to any existing article or feed. It is the most bottom-up form of agenda setting. 

AI Infra Capex is a "Rock and a Hard Place" Situation

Investors have been jittery about possible artificial intelligence circular investment; overinvestment; high capital investment and the sustainability of end user demand for many months, and late October seemed to crystalize some of those fears as key hyperscalers reported quarterly earnings. 

Looking at September 30 through October 30, 2026, one can see the double-digit declines. For about the week ending October 30 technology stocks lost about $1 trillion in valuation.  

Company	Ticker	October stock-price change	Reported Oct. 30 market value ($ billion)	Estimated start-of-month market value ($ billion)	Estimated market value lost ($ billion)
Alphabet	GOOGL	−18.67%	$3,440	$4,230	$790
Nvidia	NVDA	−10.39%	$4,930	$5,502	$572
Meta Platforms	META	−15.19%	$1,570	$1,851	$281
TSMC	TSM	−10.97%	$2,110	$2,370	$260
Micron Technology	MU	−16.92%	$999	$1,203	$204
Broadcom	AVGO	−7.15%	$1,560	$1,680	$120
Amazon	AMZN	−3.53%	$2,600	$2,695	$95

Losses were worse for many suppliers on the chip fabrication, memory or neocloud parts of the value chain. Investors increasingly questioned the return on AI spending. That increases pressure on hyperscalers to reverse course or moderate, but at the risk of abandoning the effort to emerge as a leader of the business. 

Company	Ticker	Reported October stock-price decline
Intel	INTC	−45.20%
CoreWeave	CRWV	−38.05%
SanDisk	SNDK	−35.58%
Nebius Group	NBIS	−33.65%
Lam Research	LRCX	−29.69%
Arm	ARM	−29.54%
Credo Technology	CRDO	−28.16%
Astera Labs	ALAB	−27.42%
Marvell Technology	MRVL	−26.74%
Applied Materials	AMAT	−26.30%

Amazon CEO Andy Jassy calls AI a “once-in-a-lifetime opportunity. I think what we're seeing with AI, it's the biggest transformation in our lifetime.” 

"Would it be more convenient if this type of acceleration and opportunity happened over 25 years and you could spread the CapEx over 25 years," Andy Jassy, Amazon CEO  asked an analyst during the (quarterly earnings) call. "Yeah, it might be more convenient, but that's not the world that we live in, and that's not the opportunity."

That does not help firms keep their profit margins, debt or cash flow where they just recently were, in the short term. But none of the would-be leaders believe they can significantly limit their capital investments at this point without forgoing the opportunity to lead the future market. 

So multiple compression seems likely, as AI infra capex seems to offer a “rock and a hard place” choice.