What Will Be the Primary AI Impact?

Right now, one might argue that the greatest impact of artificial intelligence will come in the realm of efficiency: allowing humans to do all sorts of things faster. To some extent, that has been at least a secondary feature of most computing technologies since 1980.

But it might be argued that the primary outocmes of new computing technologies have centered on digital product substitution for analog or physical products, removing constraints of time and place.

Products such as music, newspapers, magazines, books, television and movies were changed from physical to virtual products, or from physical delivery to virtual delivery. 

Since virtual goods are often cheaper to create, distribute and replicate than physical goods, new business models are possible. Video and music streaming; online publishing; user-generated content; social media and search are examples. 

Traditional Industry	New Challengers	Competitive Advantage
Retail (Brick-and-Mortar Stores)	Online Retailers (Technology)	E-commerce platforms, data analytics for targeted marketing, efficient logistics networks.
Media (Newspapers)	Social Media Platforms (Technology)	Real-time news updates, user engagement through interactive features, targeted advertising.
Taxis (Regulated Industry)	Ride-Sharing Apps (Technology)	Mobile app for booking rides, efficient matching of drivers with passengers, dynamic pricing models.
Financial Services (Traditional Banks)	Fintech Startups (Technology)	Mobile banking apps, online payment processing, data-driven credit scoring models.
Hospitality (Hotels)	Home-Sharing Platforms (Technology)	Online booking platform, user reviews and ratings, lower lodging costs for travelers.

Aside from new “products,” we also saw at least a few new business models, such as ad-supported technology, which did not exist prior to the internet. You might not think of social networks; messaging or search as “technology” products, but they are. Likewise, we saw the development of commerce-supported technology models as well. 

Though AWS and Google Cloud might use a traditional fee-for-service revenue model, that is possible only because the prior creation of ad-supported search and commerce produced excess capacity that underpinned cloud computing “as a service.”

Likewise, earlier waves of innovation removed time and place constraints. E-commerce allows shopping anytime, anywhere while communication tools such as messaging, email and video conferencing enable collaboration across great geographical distances almost for free. 

So how might AI alter business models, consumer experience and industries? Right now, it seems as though extreme personalization; customization and automated functions will be the primary effects. 

AI will further personalize software experiences, creating hyper-personalized experiences for consumers, and therefore supplier opportunities across many industries. 

Automation and efficiency should be the other key AI contribution, allowing firms to optimize and reduce costs across their operations. Aside from the consumer price benefits, that will enable new possibilities for cross-industry disruption. 

Cloud computing “as a service” allowed Amazon (retailer) and Google (search provider) to emerge as suppliers of computing services in competition with traditional suppliers of computing hardware and software, for example. 

Microsoft, until recently a primarily a supplier of enterprise and consumer software, emerged as a supplier of computing services and content. Apple the PC company became a leading mobile phone supplier. 

Cable TV firms became full-fledged suppliers of fixed and mobile communications services. Many non-banks essentially became “banks.” 

Traditional Industry	Firm	Services Offered
Retail (Large Chains)	Walmart MoneyCard, Amazon Cash	Prepaid debit cards for purchases and bill pay.
Technology (Payment Apps)	PayPal, Venmo, Square	Money transfer, bill pay, debit card linked accounts.
Retail (Fintech Startups)	Chime, Current, SoFi	Mobile banking accounts, debit cards, potential credit products.
Retail (Fintech Startups)	Klarna, Afterpay	Point-of-sale financing and "buy now, pay later" options.
Finance (Investment Firms)	Charles Schwab, TD Ameritrade	Robo-advising, checking accounts, debit cards.
Retail (Ride-Sharing)	Uber Debit Card	Debit card with rewards and features for drivers.

It is hard to tell, at this moment, whether AI will enable entirely new categories of products and services in the same way that the internet produced “search” and “social media,” with their different revenue models. 

All we know now is that AI will  be applied to virtually every existing industry, business process and consumer product in some way. So AI will be a feature of most products; an application in other cases. AI will have vertical industry forms, where AI-optimized processes are industry-specific, as well as horizontal applications supporting marketing, operations or finance for any industry. 

AI might in some cases be used as an interface, in the same way that graphical user interfaces changed the human interaction with personal computers. In other cases AI might be an alternative replacement for “search.” 

There are lots of other analogies. Generative AI, for example, might function as a word processor; a photo editing app; a musical instrument; a mini version of an operating system, human subject matter expert or code writer. 

Generative AI Function	User Analogy	Description
Text Generation	Word Processor	Instead of typing from scratch, AI generates different creative text formats like poems, scripts, musical pieces, or code based on prompts and user input.
Image Generation	Photo Editing App	AI acts like a powerful photo editor that can create new images from scratch based on descriptions or edit existing ones by adding elements or changing styles.
Music Generation	Musical instrument	AI generates new music pieces in various genres or moods based on user preferences.
3D Modeling	CAD Software	Like Computer-Aided Design (CAD) software, AI can generate 3D models of objects for various purposes, from prototyping to video game design.
Data Augmentation	Operating System	Imagine an OS feature that automatically creates synthetic data (like images or text) to supplement existing datasets, improving the training of other AI models.
Personalization	App Feature	Think of an app feature that personalizes your experience. Generative AI can personalize content feeds, product recommendations, or even tailor learning materials based on individual user preferences.
Code Completion	Programming Language Feature	Similar to a programming language's code completion feature, AI can suggest or even generate entire sections of code based on the context of the program being written.
Creative Ideation	Brainstorming Session Assistant	Imagine having an AI assistant during a brainstorming session. It can generate new ideas, variations on existing concepts, or unexpected connections to spark creative thinking.

AI might be described as a machine-based system that can make predictions, recommendations, or decisions. 

Machine learning then might be defined as data-driven approaches that allow computers to learn from data without being explicitly programmed. 

Neural networks are computer systems inspired by the structure and function of the human brain, able to learn from data and improve their ability to perform tasks such as image and speech recognition, as well as natural language processing. 

So neural networks underlie generative models designed to create entirely new content, including text, images, videos, music or software code. 

source: Wikipedia

Generative AI: App, Interface, Feature, Platform?

One reason it often is hard to categorize generative artificial intelligence is that it can assume different sorts of roles. When using Gemini, it can appear to be an application: you ask it questions and it answers. 

But many would note that Gen AI is also sometimes analogous to a graphical user interface: a way to interact with a computer and its resources. Like a GUI, generative AI acts as an intermediary between the user and the underlying complexities of a system. It translates user intent into specific instructions for the computer to generate desired outputs (think image generation based on text descriptions), using natural language. 

But sometimes, Gen AI might operate as a platform, supporting many other applications and use cases. At other times, it might seem to be an operating system, allocating resources or workflows. 

Perhaps most often, Gen AI will be a feature of any existing application. 

Generative AI Use Case	Analogy	Example
Text-to-Image generation	GUI	A design tool where users describe an image concept and the AI generates different visual options.
Music generation based on genre or mood	Application	A standalone application that creates original music pieces based on user-specified preferences.
Custom GUI	OS	An AI system creates a customized interface based on a single user’s history of interactions
Background removal tool in photo editing software	Feature of an Application	A photo editing program that incorporates an AI feature to automatically remove the background from an image.
Resource and task management	OS	Someday GenAI might allocate resources or create scripts or workflows based on past user experience
AI-powered chatbot for customer service	Application or Feature of an Application	A virtual assistant that can answer customer questions and complete tasks in a conversational way.
AI-generated product descriptions in e-commerce	Feature of an Application	An e-commerce platform that uses AI to generate unique and creative product descriptions based on product details.
Code generation for  programmers	Feature of an Application	A development environment that uses AI to suggest or automatically complete lines of code, improving programmer productivity.

AI Might Enable More "Surge Pricing"

Surge pricing--dynamic pricing used when demand for a service spikes well above normal--seems virtually certain to become more common as artificial intelligence allows suppliers to adjust real-time prices to balance supply and demand.

Most of us seem most familiar with the concept as applied to prices for ridesharing services during rush hour, or at times of inclement weather, when demand for rides exceeds the supply of drivers and vehicles. In principle, surge pricing works by reducing rider demand by temporary price boosts, while providing incentives for additional drivers and vehicles. 

Older examples of dynamic pricing--though not “surge” pricing--are airline ticket prices closer to time of departure or concert tickets closer to time of performance.  

Consumers might not be too happy about surge pricing as applied to other products, such as restaurant meals, for example. Perhaps the biggest objection with surge pricing is the feeling of being exploited. 

Lack of transparency also can be an issue. Consumers may feel like they're being charged arbitrarily, without understanding the justification.

Humans seem to have a strong sense of fairness. When prices jump significantly for the same service with no perceived change in quality, it feels unfair, regardless of economic justifications.

Also, people are more sensitive to losses than gains. So a $20 surge on a ride feels like a bigger deal than a $20 discount feels good.

Surge pricing can disconnect the perceived value of a service from its actual value. If a ride during rush hour feels no different than a regular ride, paying extra can feel unjustified. But “value” might be “I make my flight” rather than “I get to the airport.” 

But it might be reasonable to note that people dislike pierce uncertainty in general. It is not just “what am I paying” but also “what are others paying?” Without fixed pricing, one might always be concerned that others are paying less. 

Still, we are likely to see much more surge pricing as AI enables it as a way of balancing supply and demand. How it is practiced, how policies are communicated and explained will help reduce possible consumer frustration. 

As with restaurant reservations, people might accept the choices of “be seated now, or in two hours,” even when different prices for meal items, or surcharges, are required for the former; not for the latter. 

It then is simply an extension of the decisions all of us make all the time about what to buy, and under what circumstances. 

Industry	Products/Services	Example Companies	Factors Influencing Price
Transportation	* Flights * Ride-sharing * Train tickets	Airlines (e.g., United Airlines), Ride-hailing apps (e.g., Uber, Lyft), Rail companies (e.g., Amtrak)	* Time of booking * Demand (peak hours, holidays) * Weather conditions * Competition * Available seats/cars
Vacation Rentals	* Vacation homes * Apartments	Rental platforms (e.g., Airbnb, VRBO)	* Seasonality * Events in the area * Number of guests * Booking lead time * Local rental market
Lodging	* Hotel rooms	Hotel chains (e.g., Marriott, Hilton), Independent hotels	* Day of the week * Events in the city * Occupancy rate * Competitor pricing * Room type
Entertainment	* Concert tickets * Sporting event tickets * Movie tickets	Ticketing platforms (e.g., Ticketmaster), Movie theaters	* Artist/Team popularity * Seat location * Demand (closeness to event) * Competitor pricing * Weekday vs. weekend
Electricity & Energy	* Utility bills	Utility companies	* Time of day (peak vs. off-peak) * Season (higher demand in summer/winter) * Customer usage patterns * Government regulations
Communications	* Mobile phone data plans	Mobile phone carriers	* Data usage * Customer plan type * Promotional offers * Competitor pricing