AI Sustainable Advantage?

 One obvious observation always applies for applied IT: when every company has access to the same technology, the long-term competitive advantage is dulled or eliminated. That is not to say that hundreds of billions of dollars worth of  investment in AI will prove useless. 

It is to say that AI will become “table stakes.” Every firm will use it. And so sustainable competitive advantage based on AI and large language models will prove elusive. 

But what remains to be seen is whether one or a few firms will somehow gain such a first-mover lead for a variety of reasons not restricted to technology (network effects, especially) that important medium-term advantages can be sustained. . 

Think of Netflix in video streaming, Amazon in e-commerce, Microsoft in enterprise applications, Apple in phones, Meta in social apps or Google  in search. At least in the medium term, each of those firms has built advantages over competitors, though arguably not entirely because of technology deployment. 

Firm	Market	Network effects	How network effects have helped the firm gain market leadership
Google	Search	The more people use Google Search, the more data Google has to improve its search results. This attracts more users to Google Search, creating a virtuous cycle.	Google is the market leader in search, with over 90% of the global market share.
Meta	Social media	The more people use Meta's social media platforms, the more valuable they become to users. This is because users can connect with more of their friends and family on Meta's platforms than on any other social media platform.	Meta is the market leader in social media, with over 3 billion active users.
NVIDIA	Graphics processing units (GPUs)	The more people use NVIDIA GPUs, the more developers build applications for NVIDIA GPUs. This attracts more users to NVIDIA GPUs, creating a virtuous cycle.	NVIDIA is the market leader in GPUs, with over 80% of the global market share.
Apple	Smartphones	The more people use Apple smartphones, the more valuable they become to users. This is because users can access a wider range of apps and services on Apple smartphones than on any other smartphone platform.	Apple is the market leader in smartphones, with over 20% of the global market share.
Microsoft	Cloud computing	The more people use Microsoft's cloud computing platforms, such as Azure, the more reliable and scalable they become. This attracts more users to Microsoft's cloud computing platforms, creating a virtuous cycle.	Microsoft is the market leader in cloud computing, with over 20% of the global market share.
Netflix	Video streaming	The more people use Netflix, the more data Netflix has to improve its content recommendations. This attracts more users to Netflix, creating a virtuous cycle.	Netflix is the market leader in video streaming, with over 220 million subscribers worldwide.
Amazon	E-commerce	The more people use Amazon to shop, the more selection and lower prices Amazon can offer its customers. This attracts more customers to Amazon, creating a virtuous cycle.	Amazon is the market leader in e-commerce, with over 30% of the global market share.

Many would argue that what mattered was network effects, not technology in a direct sense. Following that playbook, would-be AI leaders will seek to create network effects as rapidly as possible. 

That aside, most early IT adopters who do not gain network effect benefits might gain some temporary advantage over laggards, but the advantage is not sustainable, any more than “using personal computers, spreadsheets, mobile devices, software or search engines” has proven to provide sustainable advantage. 

IT tool	Benefits from using the tool	Accessibility of the tool to other companies	Sustainable advantage?
Customer Relationship Management (CRM) software	Improved customer service, increased sales, and reduced costs	Yes, any company can purchase and use CRM software	No
Cloud computing platform	Scalability, flexibility, and cost savings	Yes, any company can subscribe to a cloud computing platform	No
Enterprise Resource Planning (ERP) system	Integrated business processes, improved efficiency, and reduced costs	Yes, any company can purchase and implement an ERP system	No
Artificial intelligence (AI) software	Increased productivity, improved customer experience, and new product development	Yes, any company can purchase and use AI software	No
Cloud computing	Scalability, agility, cost savings	Can be used to quickly and easily launch new products and services, or to scale existing products and services up or down as needed.	No
Big data analytics	Insights into customer behavior, trends, and market opportunities	Can be used to develop more effective marketing campaigns, improve product development, and optimize operations.	No
Machine learning	Automated decision making, predictive analytics, and personalization	Can be used to improve customer service, reduce costs, and increase sales.	No

AI Obliterates All Other Top IT Priorities for 2024

By now it is obvious that artificial intelligence has pushed every other information technology buzzword off “top of mind” status. The IDC FutureScape: Worldwide IT Industry 2024 Predictions, for example, were squarely organized around AI, which garnered nine of the top 10 2024 IT predictions.

In fact, the top nine items were AI-related. Satellite internet connectivity was the sole item not related to AI. 

IDC expects the shift in IT spending toward AI will be fast and dramatic, impacting nearly every industry and application. By 2025, Global 2000 (G2000) organizations will allocate over 40 percent of their core IT spend to AI-related initiatives, IDC said.

For the IT industry, AI will replace cloud as the lead motivator of innovation.

As always has been predicted, winners will be those with access to huge amounts of data. 

By 2025, 40 percent  of services engagements will include GenAI-enabled delivery, impacting everything from contract negotiations to IT Ops to risk assessment.

Report	Firm	Key findings
AI in the Enterprise: The Next Frontier	Gartner	AI will drive IT industry priorities in 2024, with a focus on automation, personalization, and decision intelligence.
AI Trends 2024	IDC	AI will become more pervasive in IT in 2024, with a focus on developing and deploying AI-powered applications and services.
The Future of AI in IT	McKinsey & Company	AI will have a major impact on IT in 2024, with a focus on improving efficiency, productivity, and innovation.
AI for IT Operations	Forrester Research	AI will be used to automate and optimize IT operations in 2024, leading to reduced costs and improved performance.
The AI Imperative for IT	Deloitte	IT organizations need to embrace AI in 2024 in order to remain competitive and meet the demands of the digital age.
Top 10 Strategic Technology Trends for 2024	Gartner	Democratized generative AI is one of the top 10 strategic technology trends for 2024. Generative AI is becoming more accessible and affordable, and it is driving innovation in a wide range of industries.
Predictions 2024: The AI Era Accelerates	Forrester	AI is accelerating the digital transformation of businesses in all industries. By 2024, AI will be a core part of every business's IT strategy.
Technology Trends Outlook 2023	McKinsey	AI is one of the most important technology trends of our time. AI is already having a major impact on the IT industry, and its impact is only going to grow in the coming years.
Technology Vision 2023	Accenture	AI is one of the five key technology trends that will shape the future of business. AI is driving innovation in a wide range of areas, including customer experience, product development, and operations.
FutureScape: Worldwide IT Industry Predictions 2024	IDC	AI is one of the top 10 IT trends for 2024. AI is transforming the way businesses operate and deliver value to their customers.
Gartner's Hype Cycle for Emerging Technologies, 2023	Gartner	Artificial general intelligence (AGI) and generative AI are two of the top five emerging technologies in 2023, with AGI expected to reach the Plateau of Productivity in 2028 and generative AI expected to reach the Plateau of Productivity in 2026.
Forrester's Predictions 2024	Forrester	Forrester predicts that by 2024, 80% of new enterprise applications will be AI-enabled.
McKinsey's Technology Trends Outlook 2023	McKinsey	McKinsey predicts that AI will generate $13 trillion in global economic activity by 2030.
Accenture's Technology Vision 2023	Accenture	Accenture predicts that by 2025, 98% of global executives will believe that AI models play an important role in their organizations.
IDC's FutureScape: Worldwide AI Predictions 2023-2027	IDC	IDC predicts that by 2027, global spending on AI will reach $845.8 billion.

Amazon Uses Multiple AI Models

Amazon is pursuing several artificial intelligence efforts, each apparently with a different monetization model. Some models are intended to support Amazon’s own e-tailing efforts, while others are aimed at third-party enterprise customers buying cloud computing capabilities. 

Among the latest efforts is a new large language model, codenamed Olympus, intended to be sold to AWS cloud computing customers and obviously pitched as a competitor to Google and Microsoft LLMs. 

Olympus is expected to perform better than Titan, another group of Greek-named LLMs that AWS is currently selling to cloud customers. Up to this point, AWS has been selling the use of Titan to develop applications with personalization and search capabilities. 

Two other Titan models, designed to help customers create apps that generate text or summarize long tracts of text do not appear broadly available to AWS customers, some note. 

AWS also has been supporting LLMs developed by Anthropic, as well as other models customers might prefer, such as the AI21 Labs LLM. 

But Amazon also has been using AI to support its own retail operations as well. 

There are several key e-commerce use cases.. Amazon monetizes its product recommendations by increasing sales.

Amazon also monetizes its AI-powered search results by displaying sponsored products at the top of the search results page. Advertisers pay Amazon to display their products in sponsored search results.

Amazon monetizes its AI-based fraud detection by preventing financial losses.

Amazon monetizes its inventory management by using AI to reduce costs while Amazon monetizes its logistics network by reducing costs and increasing sales by charging partners to use the network. 

AWS has different monetization avenues, all based on offering cloud computing services to enterprises and other customers. For example, AWS monetizes its machine learning services by charging customers for usage.

Natural language processing services can be purchased by AWS customers as well. Customers are charged based on the number of API calls that they make and the amount of data that they process. So that particular AI service earns revenue for AWS based on usage. 

Likewise, AWS monetizes its computer vision services by charging customers for usage. Similarly, AWS monetizes its speech recognition and synthesis services by charging customers for usage.

Amazon monetizes Alexa by selling Alexa-enabled devices and services. Amazon also generates revenue from advertising on Alexa devices and services. Amazon also monetizes Amazon Robotics by selling robots to other companies.

All of those features and services are forms of AI-enabled products. 

Name	Target Audience
Megatron-Turing NLG	Researchers and developers working on natural language processing (NLP) and machine learning (ML) tasks, such as text summarization, question answering, and machine translation.
Amazon Comprehend	Businesses and developers who need to extract meaning from text data, such as customer reviews, social media posts, and product descriptions.
Amazon Rekognition	Businesses and developers who need to extract meaning from images and videos, such as identifying objects, detecting faces, and understanding scenes.
Amazon Transcribe	Businesses and developers who need to transcribe audio to text, such as customer calls, meetings, and lectures.
Alexa	Consumers who use Alexa-enabled devices to control their homes, get information, and access entertainment.

--------------------

Shopify Illustrates Indirect AI Monetization Model

E-commerce use cases for artificial intelligence often will be indirect, as now is the case for 

Shopify Magic, the AI platform now offered to Shopify merchants.

The company says Shopify Magic "is a suite of free AI-enabled features that are integrated across Shopify's products and workflows to make it easier for you to start, run, and grow your business." In other words, the AI monetization comes from the fees Shopify earns from its customer retail operations. 

In 2023, about 68 percent of Shopify revenue was generated by Merchant Solutions, while about 32 percent comes from subscriptions.

In 2022, Shopify's revenue came from the following sources, subscriptions and merchant services, of which about 71 percent of total revenue was generated by Merchant Solutions. That includes such features as: 

• Shopify Payments, a payment processing service that allows merchants to accept credit and debit cards online and offline.

• Shopify Shipping: A shipping solution that helps merchants get discounted shipping rates from major carriers.

• Shopify Capital: A financing solution that provides merchants with loans to help them grow their businesses.

• Shopify App Store: A marketplace where merchants can find and install apps to add new features and functionality to their stores.

• Shopify Partners: A program that connects merchants with experts who can help them with everything from design and development to marketing and customer service.

Shopify also makes money from Subscription Solutions, which generate about 29 percent of total revenue. Think of that fee as similar to a mobile subscription, providing merchants access to the Shopify platform, as well as features and tools to help them create and manage their online stores.

Use of Shopify Magic is provided at no additional charge. Shopify expects to monetize the features by attracting more merchants who will use more Shopify services, including payment processing, shipping and finance, for example.

So Shopify monetizes indirectly, by gaining and retaining more customers, while generating fees for providing a variety of services supporting their retail customer sales.

Using Customer Twin Models Beyond Recommendations

“Customer twin models” (a digital twin model focused on consumer behavior) seem to be finding early application for retailers selling products and content. The obvious early use case is recommendations based on prior behavior. 

Amazon uses customer twin models to personalize product recommendations, for example, based on past buying and searching behavior. 

Netflix uses customer twin models to recommend movies and TV shows. Spotify uses customer twin models to personalize music recommendations.

Since a customer twin is a virtual representation of a real customer, using data including demographic information, purchase history, website browsing behavior, and social media activity, other business use cases seem viable as well. 

Customer twin models can be used to create personalized experiences across various touchpoints, including websites, mobile apps, and in-store interactions. Businesses can tailor content, navigation, and product offerings to each customer's unique needs. 

Such models can enable businesses to proactively identify customers who are at risk of churning or dissatisfaction.

Customer twin models also can be used to detect fraudulent activities, such as unauthorized transactions or account usage anomalies, or to optimize marketing campaigns and improve customer acquisition strategies.

They can additionally be used to develop predictive analytics models that forecast future customer behavior, such as purchase intentions, product preferences, and potential churn. 

The models also can enable businesses to offer personalized pricing and promotions tailored to each customer's unique needs and purchase history.

In the telecommunications industry, customer twin models can be used to optimize network performance and capacity planning. By analyzing customer usage patterns and traffic data, telecommunications companies can anticipate demand fluctuations and allocate resources accordingly.