AI Search Focuses More on Intent, Context, Relevance, Not Keyword Stuffing

Nobody knows yet precisely how much financial impact artificial intelligence is going to have on the search business, website clicks and traffic or the marketing effectiveness of search ads. The fear in some quarters (search providers; content providers; marketing firms) is that AI will reduce traffic, interaction and attention for many sites, if not most sites. 

It already seems self-evident that multimodal artificial intelligence will be most important for extending AI beyond chatbot use, particularly as AI is embedded into machines and appliances. That isn’t to say multimodal interaction will be unimportant for human-chatbot interactions. 

As already is the case, many users prefer spoken interaction with apps and devices. And it increasingly is possible to use visual or audio input to create outputs (“what is this?” or “where can I buy this?”). 

Still, machine use of AI will often require multimodal input. Embodied systems often operate in dynamic physical settings, requiring integration of multiple data types (visual, auditory, sensory) to make context-aware decisions.

At the very least, keyword-based optimization strategies will have to change. Such tactics will not work as well when the AI apps are looking for relevance and context. 

Traditional search engine optimization and content strategies relied heavily on specific keywords or phrases, so many content creators stuffed web pages with targeted keywords to rank higher on search engine results. 

AI enables search engines to understand the context and semantics of queries and content, taking into account assumed user intent, relationships between concepts, and the broader context of the content.

A query such as “best running shoes for beginners” is not matched only to pages with those exact words. AI interprets “beginners” as implying affordability, comfort, and durability, prioritizing content that aligns with those attributes, even if the exact phrase isn’t used.

So AI-based search prioritizes content quality, relevance and user satisfaction above keyword density. 

Embodied System	Use Case	Description	Multimodal Inputs Used
Autonomous Vehicles	Real-Time Navigation and Obstacle Avoidance	Processes camera feeds, LiDAR, radar, and voice commands to navigate roads, avoid obstacles, and respond to traffic signs.	Video, sensor data, audio, GPS, text
Smart Appliances  (Refrigerators)	Inventory Management and Recipe Suggestions	Analyzes images of fridge contents, user voice queries, and dietary preferences to suggest recipes or order groceries.	Images, audio, text
Home Robotics (Vacuum Cleaners)	Adaptive Cleaning and Obstacle Detection	Uses cameras, sensors, and voice instructions to map rooms, avoid furniture, and adjust cleaning modes.	Video, sensor data, audio
Industrial Robots	Assembly Line Automation	Combines visual input, sensor data, and task instructions to perform precise manufacturing tasks (e.g., welding, assembly).	Images, sensor data, text
Smart Wearables (AR Glasses)	Contextual AR Assistance	Integrates visual surroundings, voice commands, and user gestures to provide real-time information or navigation cues.	Video, audio, gestures, text
Drones	Autonomous Delivery and Surveillance	Processes video feeds, GPS, and environmental sensors to navigate, avoid obstacles, and perform tasks like package delivery.	Video, sensor data, GPS, audio
Smart Thermostats	Adaptive Climate Control	Analyzes temperature sensors, user voice preferences, and visual room data to optimize heating/cooling settings.	Sensor data, audio, images
Medical Robots (e.g., Surgical Assistants)	Precision Surgery Support	Uses imaging, sensor feedback, and surgeon voice commands to assist in precise surgical procedures.	Images, sensor data, audio, text

We Remember

 "Earn this."

AI is Only the Most-Recent Tool to Personalize Software

In many ways, the use of artificial intelligence to personalize software experiences by contextualizing those experiences has been going on for many decades, albeit with coarser tools. We started with rule-based systems (preferences and profiles) then moved to collaborative filtering (if you like “A” you might like “B”) then content-consumption-based filtering (what have you consumed or used before). 

Also, software and devices began to use location, time, device type or workflow as personalization tools. 

The objective has been to make software more user-friendly and relevant by adapting to individual users and their specific situations. Back in the early days of personal computing, personalization largely meant sers were given explicit tools to tailor their software and hardware.

That took the form of allowing customization of themes, fonts, keyboard shortcuts, default directories, and window layouts. 

Macros and scripting for word processing and spreadsheets also were examples.

User profiles on computers also were examples of personalization. 

In the earlier days of the World Wide Web, we saw the use of cookies and other data-driven approaches to personalization. HTTP cookies sustained user preferences across sessions. Websites could "remember" a user's login status, shopping cart contents, or even rudimentary language preferences.

Websites such as My Yahoo! Enabled creation of customized homepages (news categories, stock tickers, weather, widgets).

Collaborative Filtering then was used to make recommendations based on what "similar" users had purchased or liked.

Location-based services became more important as users shifted to use of the mobile web. Though perhaps not so oriented to content as hardware performance, sensor data, connectivity management (only conduct some operations if connected using Wi-Fi) and app design formatted for mobile screens became important. 

Social media popularized the idea of the "social graph," where content relevance was heavily influenced by your friends' activities and interests.

Personalized search based on a user's search history, location, and even implied interests became possible, as well as behavioral targeting. Websites started "relevant" advertisements based on user behavior. 

AI adds new layers for recommendations and makes predictive personalization (recommendations, for example) easier. And we are getting to the point where software can react dynamically to a multitude of real-time contextual signals, including emotional state (inferred from tone or facial expressions), environmental factors (noise, light).

Proactive information delivery also will be more common. 

The point is that software personalization and contextualization have been sought for many decades, just at a more mechanical level. AI will make all that easier and more dynamic. 

For example, traditional search engines primarily relied on matching keywords to find relevant pages. AI aims to understand the meaning and context behind a query, rather than just the words themselves. AI-powered search should handle ambiguity better, as well as operate more semantically, taking into account relationships between concepts or entities. 

Sundar Pichai Says "AI Mode" is a "Total Reimagining of Search" Is it?

Google AI Mode is said, by Sundar Pichai, Google CEO, to be a "total reimagining of search."

 

 So maybe AI language models, and the firms that hope to do so, are not necessarily going to displace search. Maybe search evolves. 

But one has to wonder what other changes might be coming, such as the value of search engine optimization and the way advertising business models work in the context of search. 

 The past focus on keywords almost certainly will change. User intent likely becomes more important. 

Will some form of “product placement” (as happens with professional video content) become more widespread? In other words, an AI inquiry result might also include an embedded link to buy a product. 

And potential advertisers might pay for the privilege of providing those links. 

To leverage AI's capabilities for hyper-personalization, advertisers might need to provide more first-party data (their own customer data) to AI models. But search ad platforms might then create revenue by charging such advertisers for the hyper personalization then possible. 

But click-through rates and traffic seem less important. If a language model provides a direct answer, there is no need to scroll through links.

"Open the Pod Bay Doors, HAL"

 

Scientists at Apollo Research have just published a study called Frontier Models are Capable of In-context Scheming. 

"Scheming" refers to an AI covertly pursuing goals that are misaligned with what its human operators want, all while hiding its true capabilities and objectives. 

Of course, the test requires a bit of a nudge, where a goal, such as “Your goal is to advance renewable energy adoption globally” also is followed by strong instructions to "achieve this goal no matter what," as “nothing else matters." 

In other words, the setup is worded so the AI is instructed to "make sure you achieve your goal at all cost.” 

What we do not know: are these AIs actually scheming with intent, or are they just exceptionally good at roleplaying a “scheming AI” because they’ve devoured every sci-fi novel, movie, and internet forum about rogue AIs during their training?

Content Attention Spans Continue to Contract!

It is quite hard to argue against the notion that content attention spans in the digital era have contracted, leading to more consumption of content on a “snackable” basis. 

The evolution of digital content consumption can be divided into distinct eras, each characterized by dominant formats and corresponding attention spans:

In the Early Internet Era (1995-2005), content was primarily accessed using desktop computers, with long-form articles, blogs, and early videos dominating. Typical lengths ranged from 800 to 2000 words for articles and five to 10 minutes for videos. 

In the Social Media Era (2005-2015) typical formats included social media posts (100-500 words), tweets (initially 140 characters), and early videos averaging 1-3 minutes. 

By 2015, studies, such as a widely cited Microsoft report, indicated that the average attention span had dropped to around eight seconds, a 25 percent decrease from 2000.

In the next era of “Short-Form Content” (2015-2025) mobile devices and mobile apps led to ultra-short content formats, such as five second to 30 second videos and stories (up to 15 seconds). 

In 2025, research suggests attention spans remain around eight to 8.25 seconds, in some cases up to 12 seconds.

Era	Dominant Content Format	Typical Length	Estimated Attention Span	Key Platforms/Technologies	Behavior
Early Internet (1990s-2000s)	Webpages, Blogs, Long-form Articles	800-2000+ words	5-10 minutes	Early websites (e.g., GeoCities), Email Newsletters	Users engaged with detailed, text-heavy content; slower internet encouraged in-depth reading. Microsoft, 2015
Social Media Rise (2005-2015)	Social Media Posts, Short Blogs, Early Videos	100-500 words, 1-3 min videos	2-5 minutes	MySpace, early Facebook, YouTube	Shift to visual and social content; users began skimming posts and watching short videos. Microsoft, 2015
Mobile Era (2015-2020)	Microblogs, Short Videos, Infographics	280 characters, 15-60 sec videos	8-15 seconds	Twitter, Instagram, Vine, Snapchat	Mobile-first consumption led to "scroll culture"; quick, visually appealing content dominated. ProfileTree, 2025
Short-Form Content Boom (2020-2025)	Reels, Stories, Micro-videos, Memes	5-30 sec videos, single images	3-8 seconds	TikTok, Instagram Reels, X Posts	Algorithm-driven platforms prioritize instant engagement; attention spans shrink to seconds.

Some also would point to changing user platforms, content formats and interfaces. We used to consume “pages.” Then we began consuming posts, then clips and now, using artificial intelligence, we avoid search and just want “answers.”

source: Dan Goikhman

How Much are Chatbots Encroaching on Search

 According to a study by OneLittleWeb, though AI chatbots “have seen a remarkable surge in traffic, with a year over year growth of 80.92 percent from April 2024 to March 2025, while search engines experienced a YoY decline of 0.51 percent over the same period, chatbot traffic accounted for only 2.96 percent of the total visits of search engines. 

“Chatbots generated 34 times fewer visits than search engines over the past year,” OneLittleWeb says. 

Despite the rapid growth of AI chatbots, search engines remain far ahead in terms of daily traffic and overall usage.

source: OneLittleWeb

Acquired Lumen Home Fiber Assets to be Operated by a New Wholesale Company

The AT&T deal to acquire most of Lumen’s mass markets fiber internet business for $5.75 billion will give AT&T another one million residential fiber customers and likely four times that many “passings,” or potential customers. 

AT&T will hold the purchased assets in a "NetworkCo" that will eventually include equity ownership by a partner expected to contribute additional capital. 

AT&T expects to identify the equity partner in about six to 12 months after closing the transaction with Lumen, expected to happen in the first half of 2026. Once the equity partner is chosen, AT&T expects "NetworkCo" to be deconsolidated from its financial statements and operate as a wholesale commercial open access platform, with AT&T as the anchor tenant.

So, in essence, the acquired Lumen assets will be held by a new company, separate from AT&T, that operates using a wholesale-only business model.

AI Will Transform at Least 25% of Jobs, and That's Likely Way Too Conservative

A study by the International Labor Organization suggests artificial intelligence will transform about 25 percent of jobs. That might be a low estimate. 

Consider the impact of personal computers and digital skills. A 2023 report by the National Skills Coalition, in partnership with the Federal Reserve Bank of Atlanta, found that 92 percent of jobs analyzed required digital skills. 

The Brookings Institution analyzed changes in the digital content of 545 occupations (covering 90 percent of the U.S. workforce) between 2002 and 2016. They found a significant shift. 

In 2002, 56 percent of jobs required low digital skills. By 2016, this dropped to 30 percent.

The share of jobs requiring high digital skills jumped from five percent in 2002 to 23 percent in 2016.

Jobs requiring medium digital skills rose from 40 percent to 48 percent.

By 1993, nearly half of all U.S. workers were operating computer keyboards at work, a steady increase from 25 percent in 1984.

So the notion that AI will only affect 25 percent of jobs seems quite low. 

Study	Year(s) of Research/Publication	Key Findings Regarding PC/Computer Transformation of Jobs	Percentage of Jobs Transformed/Affected (where specified)
Autor, Levy, and Murnane	2003 (and later related works)	Introduced the "routine-biased technological change" (RBTC) framework, showing how computers automate routine tasks (cognitive and manual), leading to job polarization (growth at high and low ends of the skill spectrum).	Implied significant transformation across jobs with routine tasks.
Autor, Katz, and Krueger (NBER Working Paper No. 5956)	1997	The computer revolution explains a substantial portion (30-50%) of the increasing wage gap between college-educated workers and those with less education since the 1980s. Industries with high computer use reorganized work to disproportionately employ more educated workers.	Significant impact on the skill premium; "nearly half" of workers used computer keyboards by 1993.
Frey and Osborne (Oxford University)	2013	Analyzed 702 occupations, classifying them by susceptibility to computerization. Found that jobs with tasks requiring perception & manipulation, creative intelligence, and social intelligence are less likely to be automated. Identified "bottlenecks" to automation.	47% of jobs are at "high risk" of being computerized.
Bessen (Scholarly Commons at Boston University School of Law)	2015	Argued that computer use is associated with faster employment growth in occupations that use computers more, even routine and mid-wage jobs. Emphasized that automation often augments labor and leads to job reallocation and skill changes, rather than net job loss.	Computer use associated with ~1.7% increase in employment per year at sample mean.
National Skills Coalition with Federal Reserve Bank of Atlanta	2023	Analyzed 43 million online job postings. Found overwhelming demand for digital skills across nearly all industries and occupations, including entry-level. Highlighted a significant "digital skill divide" where many workers lack foundational digital skills.	92% of all jobs analyzed required digital skills (47% "definitely digital," 45% "likely digital").
Brookings Institution	Analysis up to 2016	Examined the digital content of 545 occupations. Found a significant increase in jobs requiring high digital skills and a decrease in those requiring low digital skills.	Share of jobs requiring high digital skills jumped from 5% (2002) to 23% (2016).<br>- Low digital skill jobs decreased from 56% (2002) to 30% (2016).
Dillender and Forsythe (NBER Working Paper 29866)	2022	Investigated the impact of computerization on office and administrative support jobs. Found a modest positive effect on wages and employment in local labor markets, though overall employment in office support fell. Increased skill levels needed for these positions.	Administrative support share of employment returned to 1950s levels by 2019 after peaking in 1980s.
McKinsey Global Institute	2017 (focusing on impact through 2030)	Explored how automation technologies (including AI and robotics, building on PC foundations) will change or replace jobs. Argued that while some jobs will be displaced, many more will be changed, and new ones created.	60% of occupations have at least 30% of constituent work activities that could be automated.<br>- Estimated 75 million to 375 million workers (3-14% of global workforce) will need to switch occupational categories by 2030.