Wednesday, May 22, 2024

How Much Lag Time Before Widely-Useful Apps Develop for AI PCs?

Microsoft’s new Copilot+ artificial intelligence personal computer illustrates a common pattern in technology deployment, namely that hardware or platform capabilities have to come first before widely-used applications can be developed to take advantage of the new platforms. 


That can be seen in mobile network generations, where it can take five years or more for new applications taking advantage of the new platform to be developed, and longer for even popular innovations to be widely embraced. 


An optimistic estimate--showing app availability and not widespread use--suggests it takes two to three years for a new capability to be available on a new mobile platform, even in a relatively difficult or limited user experience, and has relatively low adoption.


Mobile Generation

Year of Introduction

Example Widely-Used App

Lag Time

3G

1998

Mobile Web Browsing

2-3 years



Mobile Email

2-3 years

4G

2010

Mobile Streaming (Music, Video)

3-5 years



Video Calling

3-5 years

5G

2019

High-Definition Video Streaming

2-4 years



Mobile Cloud Gaming

3-5 years


That same process tends to unfold for other innovations as well. The new CoPilot+ AI PC will require 16 GBytes of RAM and 256 GB of storage, for example. The device also will require an integrated neural processing unit with performance rated at 40 trillion operations per second. 


Right now, the only application that really takes advantage of that processing power is the “Recall” feature, that indexes and retains (on the PC) an image of each page a user has opened, for about three months of activity. 


The feature is touted as allowing users to find content they have viewed recently. Early on, most users might not find that one use case compelling enough to cause them to buy a new PC. But, over time, use cases could develop that do provide high value, even if we do not know what they are, yet. 


The point is that, for most users, it might be some time before the utility of an AI PC is obvious. 


Study

Platform

Timeframe for New Applications

Key Observation

"The Rise of Killer Apps: How Killer Apps Emerged from Past Technological Shifts" by Hyejin Kim

Mobile Phone Camera

3-5 years

Points to the launch of the iPhone's App Store in 2008 as a turning point for mobile camera apps.

"The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail" by Clayton M. Christensen

Various Technologies

Varies

Emphasizes the disruption caused by new technologies and the challenges established firms face in adapting.

"AI Revolution: Road to Superintelligence" by Tegmark, Max

Artificial Intelligence

5-15 years

Discusses potential timelines for AI advancements but acknowledges significant uncertainty.

"Gartner Hype Cycle: Special Report" by Gartner

Various Technologies

Varies

Introduces the concept of the Hype Cycle, which illustrates the phases new technologies go through, including a trough of disillusionment before widespread adoption.


That has been the case for prior innovations related to the PC, such as the graphical user interface. That also was the case for the hobbyist phase of the personal computer evolution. 


Study Title

Platform

Timeframe for New Applications

"Dealers of Lightning: Xerox PARC and the Dawn of the Computer Age" by John Seabrook [Book]

Graphical User Interface (GUI) with Xerox Alto

10-15 years

"From Mainframes to Micros: A History of the Personal Computer" by Paul E. Ceruzzi [Book]

Personal Computers (pre-Apple II)

3-5 years

Tuesday, May 21, 2024

Apple's First "AI" Moves in June 2024?

Apple's Worldwide Developers Conference (WWDC) is typically held in June 2024, and that might provide an indication of what Apple is working on in the generative AI area. 


Given the importance of Siri, it would not come as a surprise to hear something about Siri AI features. Other areas where Apple’s on-board processing approach could support AI could include summarization features, photo editing or chatbot features. 


All that would make sense with the arrival of iOS 18, the next major update to the iPhone operating system.


Though Apple is widely considered to be “behind” in generative AI leadership, that perception is likely misplaced. Recall Apple’s traditional approach to technology innovation: it rarely is the “first” to deploy any new technology. Instead, it has excelled at packaging new technology in better, more user-friendly or elegant ways. 


In fact, it would have been a shock had Apple emerged early as a generative AI leader. 


Where Apple should emerge as a force is on-device AI, given its leadership in devices and device functions, where AI already has been deployed to support smartphone operations related to imaging and cameras; user voice input; voice-to-text translation or facial recognition. 


Use Case

Description

Facial Recognition (Unlocking Phones)

Faster and more secure authentication compared to server-based verification.

Image/Video Processing (Filters, Editing)

Real-time filters and effects applied directly on the device, without needing to upload and download media files.

Voice Recognition (Offline Assistants)

Offline access to voice commands for basic tasks like setting alarms or making calls.

Sensor Data Analysis (Fitness Trackers)

Real-time processing of biometric data for personalized health insights and fitness coaching.

AR/VR Applications (Overlays, Interactions)

Enhanced responsiveness and lower latency for a more immersive augmented or virtual reality experience.



On-Device AI Processing Advantage

Value

Faster Response Times

No need to send data back and forth to the cloud, leading to quicker results, especially for real-time applications.

Lower Power Consumption

Processing data locally reduces reliance on network connectivity, saving battery life on mobile devices.

Improved Privacy, Security

User data stays on the device, minimizing privacy concerns and potential security risks associated with cloud storage.

Lower Power Consumption

Less reliance on cloud servers translates to better battery life for personal devices.


Offline Functionality

Works even without an internet connection, essential for situations with limited access.


Offline Functionality

AI features can still function even without an internet connection, crucial for areas with unreliable network coverage.



Has AppleTV+ "Failed?"

Some might argue that Apple TV has “failed,” in the sense of not being a leading video streaming provider in terms of market share. 


But companies in the same industry often have very-different business models, and often, some revenue-producing segments of those businesses exist in large part to support the core revenue model. 


Apple TV, for example, arguably exists to support Apple’s core hardware sales model. Microsoft’s videogame business arguably is used to support the firm’s hardware sales. Amazon’s streaming video service is a way of driving subscriptions for Amazon Prime e-commerce services. 


Also, some might argue that streaming also contributes to the “services” revenue streams that most believe are the key to Apple’s future growth. 


Business Model


Core Revenue Model

Supporting Products/Services

Examples


Hardware Sales

Selling physical IT equipment

Personal computers (desktops, laptops) - Servers and storage  Networking equipment (routers, switches) - Mobile devices (smartphones, tablets)

Dell Technologies (DELL) - HP Inc. (HPQ)  Apple (AAPL)

Software Licensing

Selling licenses to use software applications

Perpetual licenses (onetime purchase) - Subscription licenses (monthly/yearly fees)  Open-source software with paid support plans

Microsoft (MSFT) - Adobe (ADBE)  Red Hat (RHT)

Software as a Service (SaaS)

Subscription-based access to cloud-hosted software

Web-based applications (CRM, project management, email) - Scalable and flexible deployment options - Integration with other cloud services

Salesforce (CRM) - Zoom Video (ZM) - Dropbox (DBX)

Platform as a Service (PaaS)

Providing a platform for building and deploying applications

Development tools and frameworks - Infrastructure and services (compute, storage, databases) - Integration with other cloud services

Amazon Web Services (AWS) - Microsoft Azure (MSFT) - Google Cloud Platform (GOOG)

Infrastructure as a Service (IaaS)

Renting out virtualized computing resources

Servers, storage, networking - Scalable on-demand resources - Pay-as-you-go pricing model

Amazon Web Services (AWS) - Microsoft Azure (MSFT) - Google Cloud Platform (GOOG)

Managed Services

Providing ongoing management and support for IT infrastructure

System administration - Network monitoring and security - Cloud management services - Help desk support


IBM (IBM) - Accenture (ACN) - DXC Technology (DXC)

Value-Added Reseller (VAR)

Reselling hardware and software with additional services

Integration and customization services - Training and implementation support - Ongoing maintenance and support

Insight Enterprises (NSIT) - SHI International Corp.

Freemium

Offering a basic version for free with premium features for a fee

Freemium software applications - Mobile apps with in-app purchases - Subscription upgrades


Spotify (SPOT) - Evernote (EVER) - LinkedIn (MSFT)

Advertising-Based

Generating revenue through advertising displayed to users

Free online content (news, videos, social media) - Targeted advertising based on user data - Freemium model with ad-supported basic tier

Meta Platforms (META) - Alphabet (GOOG) (YouTube) - The New York Times Company (NYT)


Some might see Apple TV as a failure, within the “video streaming service” category. But as with Amazon Prime Video, success in that category is not really the point. Support for the core business model is the point.


Apple has had appliances that failed commercially, the point being that product failures can occur for products or applications. So can successes that support the core business model, such as the App Store supporting iPhone sales.


Device Name

Year Released

Reasons for Failure

Apple III

1980

High price point - Buggy operating system - Hardware problems

Apple Lisa

1983

Extremely high price point - Limited software library for a business-oriented machine

Macintosh Portable

1989

Bulky and heavy design - Short battery life - High price tag

Newton MessagePad

1993

Inaccurate and frustrating handwriting recognition - High price for limited functionality

Power Macintosh G4 Cube

2000

Unique but impractical rounded cube design - Limited upgradeability - High cost

eMac

2002

Confusing branding (targeted towards education but lacked features for mainstream users) - Limited software compatibility

iPod Hi-Fi

2006

Niche market - High price compared to portable MP3 players with speaker docks

AirPower

(Canceled in 2019)

Technical challenges in creating a wireless charging mat for multiple devices simultaneously

HomePod (Original)

2018

High price compared to competitors - Limited features for the price - Siri voice assistant not as advanced as competitors



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