Can GenAI Replace Search?

Many seemingly believe a Gartner analyst opinion that AI queries will replace search, an obvious enough conclusion for those who use generative AI engines routinely.

Some might even agree that “by 2026, traditional search engine volume will drop 25 percent, with search marketing losing market share to AI chatbots and other virtual agents,” according to Alan Antin, Gartner analyst. 

Somewhat obviously, such a shift--at some scale--will potentially reshape organic and paid search as venues for marketing spend. But ask yourself: does GenAI fundamentally change the experience of “lean forward” media as compared to “lean back?” Does GenAI turn “lean forward” into “lean back” or vice versa?

It remains true that each successive wave of electronic media has shifted marketer spending, from radio to broadcast TV to cable TV to the internet, search engine marketing, social media advertising and mobile advertising. 

As virtual environments such as the metaverse are commercialized, advertising will migrate there as well.

To some extent, these shifts were zero sum games: what one emerging industry gained, the legacy media lost. The issue with generative or any other form of AI is the degree to which hybrid use models will emerge, where AI-assisted ad placement and formats develop as part of all existing venues. 

In other words, if AI becomes a core feature of search, social media, gaming, productivity apps, digital content venues and shopping, does GenAI necessarily disrupt, or might it disrupt and shift some amount of activity, but also reinforce existing venues and methods? 

In other words, does GenAI used “as an app” develop as a “new medium” or does it mostly remain a feature of existing media? 

To be sure, some might believe GenAI could revolutionize media by creating entirely new forms of storytelling, entertainment, and information dissemination. 

Others might see that as a remote possibility, with the more likely impact being the reshaping of all existing media. 

For example, GenAI might enable new forms of “interactive fiction,” where users experience narratives that adapt to user choices, generating personalized storylines and branching paths in real-time. Keep in mind that this also was expected for legacy media, by analysts considering the rise of interaction itself. Not so much has really changed, save for gaming use cases, though. “Interactive TV” has flopped, for example. 

AI-powered characters in games whose behavior is personalized for each user are more likely to happen, as is the application of GenAI to create metaverse and augmented reality experiences. But none of those are examples of media replacement. 

In other words, some of us would not agree that “search marketing” is exposed to replacement by use of GenAI. 

GenAI is most likely to modify existing media formats, making them more personalized, interactive, and immersive. Based on what happened with interactive TV (or storytelling in general), it seems unlikely that a brand new medium will emerge from GenAI. 

To the extent that GenAI becomes a core feature of search, social media and nearly all other experiences and apps, GenAI might not actually be a “threat” to search. 

Think of the established categories of “lean forward” experiences such as interacting with a PC or smartphone to the “lean back” experience of video, television, movies or music. GenAI as a feature will be used mostly to create those experiences, but might not change the fundamental “lean forward” experience of work, learning, search or shopping and gaming.

Likewise, the “lean back” nature of entertainment might not be desirable for movies, video, TV or musical experiences and storytelling in general. 

The way we consume media can be categorized into two main types: lean forward and lean back.

Lean forward media require active engagement and focus. Examples include:

Playing video games

Browsing the web or using search

Using social media platforms

Reading e-books

Working on a computer

Mental State: Engaged and alert, requiring concentration.

Physical Posture: Can vary, but often involves sitting at a desk or holding a device.

Lean back media requires minimal user effort and is largely a passive experience:

Watching television

Listening to music

Watching movies

Reading a physical book

Attending a concert or play

Mental State: Relaxed and receptive, focused on enjoying the content.

Physical Posture: Often involves sitting or reclining comfortably.

If GenAI were not tightly integrated with all “lean forward” experiences, one might have a better argument for replacement. But that is unlikely to be the case. Likewise, it is not clear that GenAI changes the fundamental “lean back” experience of storytelling in the form of books, TV, video, movies, music, concerts and plays.

Even if one assumes both search and GenAI chatbots are forms of "lean forward" experience, it is very hard to see a permanent stand-alone role, as GenAI already is rapidly being incorporated into all enterprise and consumer software and experiences.

So GenAI becomes a feature of search; not a replacement.

Can AI Replace Apps?

Almost by definition, artificial intelligence devices beyond the smartphone (pins, pendants, other form factors) will need to operate “beyond apps” as the primary interface will be the spoken word and screens might or might not be used. The interface will take the form of commands or questions that generate responses that are not confined or based on use any one installed app. 

Mobile World Congress, for example, T-Mobile showed an AI phone concept created with Qualcomm Technologies and Brain.ai featuring an AI assistant that replaces apps used on the smartphone. 

But one can argue the more-logical devices to be affected are smart appliances; smartwatches; smart TVs or auto infotainment systems, where screens are small or non-existent; cannot be used for safety reasons; have high context-related interface requirements or where the range of outputs is commonly related to a few parameters: on-off; higher-lower; short answer or sensor output results. 

Voice assistants such as Google Assistant and Amazon Alexa are already replacing the need for dedicated mobile apps to control smart home devices like thermostats, lights, and appliances. 

Smartwatches and other wearable devices might rely less on touchscreens and buttons, using voice commands and gestures.  

AI-powered interfaces on smart TVs and streaming devices could become more intuitive and personalized, reducing the need for navigating through structured traditional app interfaces.

Voice assistants and AI-powered interfaces are already being integrated into car infotainment systems as well, to control various car functions or access information. 

The logic for using AI to replace apps arguably could appeal more on such devices than AI phones, even if voice interaction becomes more common on smartphones. That might especially be true when the use case requires actions using personal and financial information and permissions specific to each individual.

How Much Value Can Connectivity Providers Actually Produce Using AI with Their Data?

It is inevitable that connectivity executives will talk about the ways they can employ generative AI (large language models) to support their businesses and products, ranging from the obvious (customer support or internal information queries) to some that might not be so obvious. 

While it might be easy to envision how marketing; advertising; media; content; e-commerce; education and training; financial services or healthcare could use GenAI, it likely is harder to envision how telcos can use GenAI in areas outside customer service interactions, especially in ways directly related to revenue generation. 

The standard answers might include generation of data sets that preserve the statistical properties of real customer data but are completely anonymized, then sold to customers wanting to use such data. 

At least in principle, anonymized reports could illuminate behavioral trends such as:

• Foot traffic analysis

• Traffic patterns and congestion analysis

• Device usage patterns

• App usage trends

• Network usage

• Mobile money transactions

Some forms of network performance data could be useful in some B2B situations as well. 

• Network performance insights might be useful for infrastructure suppliers

• Benchmarking of service providers and geographic areas likewise could be useful for infra providers

In practice, it is a bit unclear how valuable such data is, given that other data sources exist that also can provide insight on location; mobility; device usage; app usage or network usage. And most connectivity providers are not suppliers of mobile money services. 

Industry	Potential Value	Examples	Source
Marketing & Advertising	- Generate personalized marketing content (e.g., ad copy, social media posts) - Create targeted advertising campaigns based on audience insights - Personalize customer experiences across channels	- Generate product descriptions, headlines, and social media content tailored to specific customer segments. - Create personalized marketing materials with high engagement potential. - Develop dynamic pricing strategies based on real-time market analysis.	- Gartner
Media & Entertainment	- Generate personalized content recommendations - Create new forms of interactive entertainment experiences - Personalize news feeds and content curation	- Recommend movies, music, or articles based on individual user preferences. - Generate personalized story outlines or even short stories based on user input. - Create interactive narratives that adapt to user choices.	- Cognite
Customer Service & Support	- Automate routine customer inquiries with chatbots - Generate personalized troubleshooting guides and FAQs - Enhance customer service interactions with natural language processing	- Develop chatbots that can answer common questions and resolve basic issues. - Create personalized knowledge base articles and FAQs tailored to specific customer situations. - Improve sentiment analysis and personalize customer interactions.	- NBER
E-commerce & Retail	- Generate personalized product descriptions and recommendations - Optimize product search and discovery - Automate content creation for marketing and advertising	- Create unique and engaging product descriptions that resonate with target audiences. - Personalize product recommendations based on individual browsing behavior and purchase history. - Generate automated social media posts and marketing materials.	- Paul Prae
Education & Training	- Personalize learning experiences for individual students - Create interactive and engaging educational content - Develop adaptive learning systems that adjust to student needs	- Generate personalized study guides and practice problems tailored to individual learning styles. - Create interactive simulations and role-playing scenarios for practical learning. - Develop adaptive learning platforms that personalize content and difficulty based on student performance.	- Pro IO
Healthcare & Life Sciences	- Generate personalized reports and insights from medical data - Assist with drug discovery and development - Analyze medical literature and identify potential treatment options	- Generate personalized healthcare reports summarizing medical history and treatment options. - Analyze large datasets of medical research to identify potential drug targets. - Assist with summarizing and synthesizing medical literature for researchers.	- Investopedia
Financial Services	- Generate custom financial reports and investment strategies - Automate financial data analysis and risk assessment - Personalize financial advice and recommendations	- Create personalized financial reports summarizing investment performance and financial health. - Analyze market trends and generate investment recommendations based on individual risk tolerance. - Develop chatbots that can answer common financial questions and provide basic financial advice.	- Jon Cooke

AI Clash Between Copyright and New Technology is an Old Tale

Every new technology brings with it new legal issues. Artificial intelligence, for example, raises copyright issues. 

It is not the first time new technology has clashed with established notions of copyright. 

When photocopying machines were commercialized, manufacturers tried to block the use of the machines for making copies of copyrighted work.

Sony tried to block the use of videocassette recorders to time shift video content for later viewing. Similar disputes erupted over the use of audiocassette tapes, music file sharing and video streaming as well. 

New Technology	Copyright Issues	Key Court Decisions
Photocopying Machines (1960s)	Mass reproduction of copyrighted materials without permission.	Fair Use Doctrine Established: Williams & Wilkins Co. v. United States (1964) established the four-factor fair use test: purpose and character of use, nature of copyrighted work, amount and substantiality of portion used, and effect of use upon the market. Copying for educational purposes could be fair use.
Audio Cassette Tapes (1970s)	Home recording of copyrighted music threatened record sales.	Audio Home Recording Act (1992): Established a royalty levy on blank audiotapes to compensate copyright holders for potential lost sales due to home recording.
MP3 Players and Napster (1990s)	Peer-to-peer file sharing enabled widespread music piracy.	A&M Records v. Napster (2001): Napster was found liable for contributory copyright infringement for failing to prevent users from sharing copyrighted music.
Streaming Services (2000s-Present)	Distribution model challenged traditional music licensing and revenue streams.	Negotiated Licensing Agreements: Streaming services like Spotify and Apple Music pay licensing fees to copyright holders based on user streams.
Digital Video Recorders (DVRs)	Shifting time viewing challenged broadcasters' control over programming.	Sony Corp. v. Universal City Studios (1984): Upheld the fair use of time-shifting for personal viewing using VCRs.

Similarly, conflicts have erupted over content, social media, search, open source software and user-generated content, for example. 

Content Issue	Copyright Issues	Key Court Decisions
Social Media Sharing	Sharing copyrighted content like photos, videos, and music raises questions of fair use and infringement.	Blurred Lines: Perfect 10 v. Amazon (2002) established thumbnails could be fair use for linking purposes. However, sharing entire works without permission is generally considered infringement. The specific context and amount used determine fair use.
User-Generated Content (UGC) Platforms	Platforms like YouTube or TikTok host user-uploaded content, potentially infringing on copyrights.	DMCA "Safe Harbor": The Digital Millennium Copyright Act (DMCA) provides a safe harbor for platforms if they remove infringing content upon notification from copyright holders. Platforms like YouTube have automated takedown systems based on copyright claims.
Software Sharing and Open-Source	Sharing copyrighted software raises concerns about piracy and unauthorized distribution.	Open-Source Licenses: Open-source licenses like GPL (General Public License) allow for modification and sharing of software code, as long as certain conditions are met. These licenses provide a framework for collaborative software development while protecting copyright.
Content Aggregation Services	News aggregators like Google News display headlines and snippets of copyrighted news articles.	Fair Use and Fair Reporting: Courts have generally allowed news aggregation under fair use for purposes of reporting and commentary. The amount and substantiality of content used are crucial factors.

Eventually we will figure out some balance between copyright and use of the new technology in non-infringing ways. But it may take a while.

"Irresistible" AI Storylines Could Prove False

Some storylines seem inevitable and irresistible, either to journalists or screenwriters. And some irresistible storylines are “always wrong.”  

In the past, it has often been argued that the United States was behind, or falling behind, for use of mobile phones, smartphones, text messaging, broadband coverage, fiber to home, broadband speed or broadband price. But the “behind” storyline has proven incorrect, over time. 

To be sure, the U.S. market rarely ranks first on any “adoption or performance” metric, for a variety of reasons related to continental-sized land mass and highly rural density, among other things. 

So elections or industry standing are seen as “horse races.”  Be prepared for many surprises, as we have often seen “early leaders” falter in the computing industry. 

So artificial intelligence is viewed through the lens of a horse race or competition with a winner, a second-place finisher and many losers. So there is a “conventional wisdom” that OpenAI and Microsoft are leading, while others, such as Google, are behind, and a few, such as Apple, are “way behind.” You’d be very hard pressed to find a journalist, a financial analyst or perhaps even some within the generative AI model industry who would dispute the characterization. 

But it might also be worth noting that lots of long-gone firms were “in the lead” in the early days of the personal computer revolution. Many firms started early in the transition from character-based to multimedia web industries faltered, and many fell quickly. 

The point is that major technology transitions tend to be littered with failures, even among firms that seemed to lead, early on. Even in more prosaic industries, such as the use of mobile phones, whole countries often can be viewed as leaders or laggards in terms of consumer adoption. But such “leads” have proven to be quite temporary. 

Still, there is something different about AI. Prior transitions in computing often were led by small startups (some of which later attained scale). The current transition arguably is led by a mix of current computing leaders and some startups. That is quite different.