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The high-performance server market illustrates an important principle in the computing markets, which is that a sufficiently-important function or appliance with high price tags creates incentives for a few big end users to shift some procurement to “do it yourself” alternatives. 

In high-performance server markets, for example, a few big end users might already have about 20 percent share. In 2023, for example, the U.S. commercial value of such products was about $53 billion annually, if we included the value of the DIY production as well as commercial sales. 

Supplier	Share (%)
Dell	25-30
HP Enterprise	20-25
Lenovo	10-15
Supermicro	5-10
Other Vendors ( IBM, Fujitsu)	15-20
DIY Solutions
Meta	5-7
Google	5-7
Other Major End Users (Amazon, Microsoft)	10-15

In the AI chip business, possibly 15 percent to 20 percent of total market share is held by DIY suppliers including Meta, Google, Amazon and Microsoft, for example. 

Supplier	Market Share (%)
NVIDIA	50-60
Intel	10-15
AMD	10-15
Google (TPU)	5-10
Other Traditional Vendors (e.g., Qualcomm, MediaTek)	5-10
DIY Solutions
Meta	5-7
Google	5-7
Other Major End Users (e.g., Amazon, Microsoft)	5-10

One might note the same trend in the area of generative AI models, where a few “end users” also are suppliers of models. 

Supplier	Share (%)
OpenAI (GPT-4, GPT-3.5)	30-35
Google (PaLM 2, LaMDA)	20-25
Meta (LLaMA)	10-15
Other Major Vendors (e.g., Anthropic, Cohere)	10-15
DIY Solutions
Meta	5-7
Google	5-7
Other Major End Users (e.g., Amazon, Microsoft)	5-10

Some Problems Have No Obvious Solutions

The EU is losing ground in research and development and in the creation of innovative technology companies with global reach, says a report commissioned by the European Commission. That is unlikely to surprise anybody familiar with the matter, as such concerns have been in place for many decades.

The report says the EU lags in artificial intelligence, cybersecurity, the internet of things (IoT), blockchain and quantum computers. 

The EU has generated fewer new lead innovators in the past decade than the United States, and that the share of EU firms in the top 2,500 global R&D companies has fallen compared to other blocs,” the study argues. 

“For instance, among leading companies in software and internet, EU firms represent only seven percent of R&D expenditure, compared with 71 percent for the U.S. and 15 percent for China; similarly, the EU only accounts for 12 percent of R&D expenditure among leading companies producing technology hardware and electronic equipment, compared with 40 percent for the U.S., and 19 percent  for China.

The study notes that the EU is home to only four of the fifty largest digital marketplaces worldwide, while the ten largest platforms serving EU citizens are owned by U.S. or Chinese companies (Alphabet, Amazon, Meta, Apple, Microsoft, X, Tencent, Alibaba, Byte Dance and Baidu). 

On the other hand, the report notes that “the EU has important capabilities, in particular, in green technologies, advanced manufacturing and advanced materials, the automotive industry and biotechnology.” 

Among the perhaps-obvious recommendations are to increase research and development spending. But many of the other recommendations are less directly related, such as increasing the quality of world-class research institutions.

“According to the Nature Index in 2022, which ranks institutions based solely on the volume of

publications in a selected list of top academic science journals, the EU has only three research institutions among the top fifty globally,” the report says. 

While praiseworthy, the effort to grow more world-class research institutions is a tough and long-term goal that many would argue is unlikely to happen in a world where leadership begets leadership. 

The computer science, semiconductor and biology industries  are typically concentrated in a small number of science and technology clusters, with leading clusters accounting for a large share of overall innovation in a country, the report notes. The EU simply has such clusters, but few in the top 10. 

According to the WIPO classification of world clusters (2023 Global Innovation Index), the EU has a similar number of clusters in the top 100 as the US and China, but only one in the top 20. None of the EU clusters appear among the top ten, while the United States has four and China has three, the report says.

But there are lots of other issues, ranging from a weaker venture capital system to the degree to which academics create private companies; overall commercialization of research and regulatory and bureaucratic obstacles. 

A slower pace of technology adoption; smaller firm size; quality of digital infra and skills also are cited as obstacles. But the list of issues is numerous, including the cost of energy; access to raw materials and digital infrastructure in general. 

For some observers, the report simply restates what critics have said for decades about EU competitiveness, 

The common litany of concerns includes the fragmentation of the Single Market impedes scale. It always is argued that the EU makes insufficient investment. Regulatory barriers are said to be too high. There are talent shortages as well.

Digital infra is not well-developed enough and competitors do better in such areas. Most of the proposed remedies, and they are numerous, would require both a huge shift of resources and significant time, something perhaps unavailable to the EU as the artificial intelligence and computing innovation cycles either appear or continue. 

Some problems might not have solutions, at least not solutions that are politically or economically feasible. And even if successful, EU “catching up” to China and the United States in many technology fields would take a long time, many would likely argue.

GenAI is Not Machine Learning

Generative artificial intelligence is different from machine learning, so the value will be different as well. GenAI value will be distinguishable from machine learning, since GenAI is optimized for content creation, where machine learning is optimized for seeing patterns in data. 

Industry	Generative AI Value	Machine Learning Value
Healthcare	Personalized medicine, drug discovery, automated medical documentation	Predictive diagnostics, patient management, anomaly detection
Financial Services	Algorithmic trading, automated report generation, fraud detection	Risk assessment, customer segmentation, credit scoring
Manufacturing	Product design, prototyping, generative design for optimization	Predictive maintenance, process optimization, quality control
Retail	Personalized marketing, product recommendations, automated content creation	Inventory management, demand forecasting, recommendation systems
Education	Custom learning content, adaptive learning platforms, automated grading	Student performance analysis, personalized learning paths, dropout prediction
Media & Entertainment	Content creation (e.g., scripts, music, art), virtual actors, automated editing	Audience analysis, content recommendation, trend prediction
Transportation & Logistics	Route optimization, autonomous vehicle development, dynamic scheduling	Fleet management, logistics planning, demand forecasting
Real Estate	Property design, virtual staging, automated property descriptions	Market analysis, property valuation, predictive maintenance
Legal	Contract generation, legal research, automated case summarization	Document review, case management, legal analytics
Marketing & Advertising	Campaign creation, content generation, personalized advertisements	Customer segmentation, ad targeting, sentiment analysis
Energy	Renewable energy solutions, smart grid development, automated reporting	Predictive maintenance, energy management, demand forecasting
Technology	Software development (e.g., code generation, bug fixing), AI model training	Predictive analytics, system optimization, anomaly detection

And It is not easy these days keeping track of artificial intelligence use cases that are examples of machine learning and which are examples of generative AI. 

Task Type	Machine Learning	Generative AI
Data Analysis	Excels at analyzing large datasets to find patterns and make predictions	Can summarize and interpret data, but not its primary strength
Prediction	Strong at making predictions based on historical data (e.g. sales forecasting, risk assessment)	Can make predictions, but often less accurate than specialized ML models
Classification	Very effective for categorizing data into predefined classes (e.g. spam detection, image classification)	Can perform classification tasks, but typically not as accurately as specialized ML models
Anomaly Detection	Excellent at identifying unusual patterns or outliers in data	Can describe anomalies, but less effective at detecting them compared to ML
Content Creation	Limited capabilities in generating new content	Excels at creating various types of content (text, images, code, etc.)
Natural Language Processing	Good at tasks like sentiment analysis and language translation	Superior at understanding context and generating human-like text responses
Decision Support	Provides data-driven insights to assist human decision-making	Can offer more nuanced, context-aware recommendations and explanations
Automation	Automates specific, well-defined tasks based on patterns in data	Can automate more complex, creative tasks that require understanding and generation
Personalization	Effective at providing personalized recommendations based on user data	Can create highly personalized content and interactions
Problem Solving	Solves specific problems it's trained for within defined parameters	Can approach novel problems creatively and propose innovative solutions

GenAI Value Varies by Job Function and Industry

Lots of people will choose different words if asked “what is the one word that best describes the value of generative artificial intelligence.” Some might cite “speed” or “scale” or “augmentation” or “automation.” All are applicable.  

At least part of the reason for differing “key value” descriptions is that GenAI arguably has different key values for different job roles and industries. What creates value for illustrators will be distinct from what creates value for software engineers. 

For example, consider GenAI as providing value in a number of different ways. As with all forms of AI, GenAI amplifies some human capability (sight, sound, taste, speech, muscle power, pattern recognition) and therefore amplifies human capabilities within business processes.

That includes:

• Productivity: By automating routine tasks like data entry, report generation, or email drafting, AI frees up human employees to focus on higher-value, strategic work.

• Creativity: It can generate new ideas, design options, or marketing content, acting as a creative catalyst for human teams.

• Decision Support: AI can process vast amounts of data to identify patterns and trends, providing valuable insights that inform better decision-making.

• Customer Experience: By personalizing interactions, generating tailored content, and providing faster responses, AI enhances customer satisfaction.

• Process Optimization: AI can analyze processes to identify inefficiencies and suggest improvements, leading to streamlined operations and cost savings.

So consider a few examples of value created by applying GenAI to creation and innovation; efficiency and productivity; collaboration or the quality of output. 

For marketers, GenAI produces ad copy. For manufacturers, it automates designs or optimizes production processes. Software developers can use it to write code. Customer service agents can offload some queries. 

Pharmaceutical companies can use GenAI in drug discovery operations. 

Industry	Focus of Generative AI Value
Marketing & Advertising	Creation/Innovation (e.g., ad copy, content generation)
Product Design	Quality of Output (e.g., optimizing designs, generating variations)
Manufacturing	Efficiency/Productivity (e.g., automating design tasks, optimizing production lines)
Software Development	Efficiency/Productivity (e.g., code generation, automating testing)
Healthcare	Quality of Output (e.g., drug discovery, personalized medicine)
Customer Service	Efficiency/Productivity & Collaboration (e.g., chatbots, automating repetitive tasks)
Finance	Quality of Output & Prediction (e.g., fraud detection, risk assessment)
Education	Creation/Innovation & Quality of Output (e.g., personalized learning materials, automated grading)
Legal	Efficiency/Productivity & Quality of Output (e.g., legal document review, contract generation)
Media & Entertainment	Creation/Innovation (e.g., scriptwriting, music composition)
Scientific Research	Creation/Innovation & Prediction (e.g., hypothesis generation, data analysis)
Construction	Efficiency/Productivity & Quality of Output (e.g., optimizing construction plans, identifying safety hazards)

The point is that GenAI value depends on the type of industry; its key tasks; as well as job roles within each industry. 

For coders, GenAI allows fast checking of code. In other instances, it is the time saved conducting research that matters. In the pharma industry, it might well be new drug discovery. For content producers the value is mostly in automating content production (producing images, video or text content).