AI "Picks and Shovels"

Investing in “picks and shovels” (companies that provide the infrastructure and tools necessary for developing and deploying AI, rather than those directly developing artificial intelligence applications or algorithms) is a standard way for investors to approach a new theme such as AI. 

 In 2024, that arguably includes semiconductor manufacturers such as Nvidia, AMD, and Qualcomm; data storage and management firms such asSeagate, Western Digital, and NetApp. Some might also mention STMicroelectronics and Analog Devices in the pick-and-shovel areas. 

Among the most-popular pick-and-shovel choices are the cloud computing “as a service” suppliers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.

Suppliers of AI development tools such as Palantir, Dataiku, and C3.ai also might be viewed as plays in the infra and tools areas. 

Cybersecurity suppliers such as Crowdstrike, Palo Alto Networks, and McAfee might also come to mind. 

For many, it might appear the obvious plays are cloud computing as a service and sellers of GPUs. 

Study Name	Publication Venue	Date	Growth Prediction (Specific Provider)	Revenue Assumptions
AI as a Driver of Cloud Differentiation	Morgan Stanley	December 2023	AWS: 30% of new revenue from AI-powered CaaS by 2027	Focus on AWS's existing AI offerings and investments in areas like machine learning and natural language processing.
The AI Cloud Race: Azure vs. AWS vs. Google Cloud	Goldman Sachs	November 2023	Microsoft Azure: 45% premium on AI-powered CaaS solutions by 2030	Emphasis on Azure's strong partnerships with AI leaders and focus on enterprise AI applications.
Unlocking the Next Wave of Cloud Growth: AI on Google Cloud Platform	Jefferies	October 2023	Google Cloud: 25% CAGR for AI-powered CaaS from 2024 to 2030	Highlights Google Cloud's strengths in big data analytics and AI talent, leading to innovative AI-powered services.
Cloud Wars 2.0: The AI Arms Race	Bank of America	September 2023	All major providers: 20-35% of total CaaS revenue from AI by 2029	Assumes gradual but persistent adoption of AI across all cloud segments, with increased demand for AI-driven personalization and optimization.
The AI Advantage in Cloud Infrastructure	Citigroup	August 2023	AWS and Azure: 30% faster growth in AI-powered CaaS compared to non-AI CaaS offerings by 2025	Predicts higher customer preference and premium pricing for AI-powered solutions, favoring established providers with strong infrastructure.
AI in the Cloud: A Market Landscape Analysis	Statista	October 2022	All major providers: 15% CAGR for AI-powered CaaS market through 2027	Steady growth in AI adoption across various industries, driven by major cloud providers' investments and marketing efforts.
The Future of AI-Powered CaaS: A Competitive Landscape	Cowen & Company	August 2022	All major providers: 20-25% of CaaS revenue attributable to AI by 2028	Focuses on the competitive landscape, suggesting differentiation through specialized AI tools and industry-specific solutions.
Cloud Providers in the Age of AI: A Growth Drivers Analysis	Credit Suisse	July 2022	All major providers: 25% of incremental CaaS revenue from AI-powered solutions by 2030	Highlights the potential for AI to unlock new revenue streams through automation, improved resource utilization, and enhanced customer experiences.
AI and the Cloud: A Transformative Partnership	Deloitte	September 2022	All major providers: 30% increase in CaaS revenue attributable to AI-powered solutions by 2026	Emphasizes the role of AI in optimizing existing CaaS offerings and driving increased customer satisfaction.
AI in Cloud Computing: The Next Wave of Innovation	PwC	August 2022	All major providers: 20% of CaaS revenue derived from AI-powered workloads by 2025	Focuses on the continuous advancements in AI technology and its potential to make AI-powered CaaS solutions more accessible and affordable.

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How Big Will AI be for Cloud Computing as a Service Suppliers?

Virtually every firm that studies the impact of artificial intelligence on “computing as a service” providers seems to believe the impact on revenue growth will be substantial. 

As always, methodology matters. Studies may vary in their expectations for the shift from private to public cloud computing, the role and significance of edge computing and estimates of AI model building and inference generation enterprises will conduct on public cloud facilities. 

Study Name	Publishing Venue	Date of Forecast	Growth Prediction
AI in Cloud Computing: A Growth Engine for CaaS	Forrester Research	2023-10-26	25-30% CAGR (2024-2030)
The AI Revolution in Cloud Services	McKinsey & Company	2023-07-12	30-40% CAGR (2024-2029)
Global CaaS Market: AI-Powered Growth and Innovation	IDC	2023-06-01	20-25% CAGR (2024-2030)
Gartner Hype Cycle for Emerging Technologies 2023	Gartner, Inc.	August 2023	AI-powered CaaS expected to reach mainstream adoption within 2-5 years, driving significant revenue growth.
Accenture Technology Vision 2023: The AI Advantage	Accenture	October 2023	AI-powered CaaS platforms to become integral to business operations, leading to a 15-20% rise in CaaS market share by 2030.
Bain & Company: Artificial Intelligence 2023: The State of AI in Business	Bain & Company	September 2023	AI-powered CaaS expected to unlock $500 billion in cost savings and $1 trillion in new revenue opportunities by 2030.
Deloitte Insights: The AI Imperative: Harnessing the Power of Artificial Intelligence	Deloitte Consulting	May 2023	AI-driven CaaS to create a $5 trillion market by 2030, with significant growth in areas like intelligent edge computing and machine learning-powered optimization.
Bank of America Global Technology Research: AI in the Cloud: A New Era of Computing	Bank of America	October 2023	AI-driven CaaS expected to be a key driver of cloud service adoption, contributing to a 25% CAGR for the cloud computing market between 2024-2030.

Global AI Electricity Consumption is an Issue, But We'll Get Better at it

Over time, lots of things can be done to lessen AI energy consumption:

*  More efficient AI hardware

*  Cooling advancements including liquid immersion or indirect evaporative cooling

*  Model optimization: quantization, pruning, and knowledge distillation to reduce the complexity of AI models without sacrificing accuracy

*  Edge computing in several forms will help as well, including on-device computations

* Scheduling of AI operations to even out workloads should help as well

Renewable energy will help a bit, at least in terms of carbon footprint. 

If AI Emerges as a General-Purpose Technology, Watch for Both Disruption and Creation

Any general-purpose technology might be envisioned as a set of layers of other technologies that build on it. Many could agree that GPTs are characterized by pervasiveness, flexibility, spillover effects and transformative impact. 

So the internet might underpin layers of core infrastructure and industries and businesses built around protocols such as TCP/IP and physical networks and industries (mobile and fixed networks, terrestrial and satellite wireless networks). 

Then there might be layers of roles and businesses supplying web technologies such as HTML, CSS, JavaScript, and related web development tools that enable building websites and web applications.

Networking technology including routers, switches, firewalls would be another layer. 

So would databases, cloud storage, and content delivery networks.

Then there would be many application and service layers for communication (e-mail, instant messaging, video conferencing, and social media platforms) e-commerce and online marketplaces, content and entertainment, social media, video and audio streaming or online gaming. 

Internet of Things businesses built around smart devices, sensors, and connected appliances, as well as many types of business software could be listed.

Some might include artificial intelligence as among the layers built on the internet. But some of us would say AI is a new general purpose technology that will create its own pyramid of technologies, businesses, industries and applications. 

Era	General Purpose Technology	Impact
Pre-Industrial	The Wheel	Revolutionized transportation, agriculture, and warfare. Led to the development of roads, carts, and other wheeled vehicles.
18th Century	The Steam Engine	Powered the Industrial Revolution, driving mechanization and mass production in factories, transportation (trains, ships), and agriculture.
19th Century	Electricity	Transformed daily life with lighting, appliances, communication (telegraph, telephone), and industrial processes.
20th Century	Internal Combustion Engine	Propelled transportation revolutions with automobiles, airplanes, and ships. Changed industries, warfare, and leisure activities.
20th Century	Electronics & Semiconductors	Enabled miniaturization of devices, leading to computers, radio, television, and countless electronic gadgets.
20th Century	The Internet	Connected the world, democratized information access, facilitated communication, and fueled e-commerce, digital services, and the knowledge economy.

And some of those roles or industries might presently be viewed as built on “internet” foundations. 

Intelligent infrastructure such as smart cities, autonomous vehicles, adaptable robotics, “personalized” healthcare, neurotechnology (brain-computer interfaces),  bionic limbs and prosthetics and much “metaverse” style immersive experiences, plus much of virtual and augmented reality, hyper-personalized content creation, AI-powered companions, precision agriculture and other use cases that might today be attributed to the  “internet” GPT might eventually be properly seen as built on AI as a GPT. 

Perhaps analogies can be seen in the Apple iPhone and Google search. Apple did not invent the smartphone or the mobile phone. But it completely reshaped the business, destroying Nokia and BlackBerry in the process as former market leaders. 

Google was not the first search engine, but it destroyed Altavista and other existing search engines in the market. The point is that many existing industries might be fundamentally reshaped if AI emerges as a GPT. 

And as has been the case before, AI might reshape and disrupt existing industries, functions and roles, in addition to spawning entirely-new industries, as all prior GPTs have done.

"It's Different This Time" is Among the Greatest Dangers in Financial Markets; Sometimes in Software and Computing

“It’s different this time” is a classic example of the sort of thinking that can underpin financial bubbles. The phrase encapsulates the belief that "misconceptions" about a few “laws of economics” exist.

One might argue the same sort of argument has been made in the information technology business from time to time. In the capacity business, there was the Enron Broadband effort to create commodity exchanges for data transport.

Critics of the model were sometimes told "you don't get it." As it turns out, skeptics were right. In the IT business, a related sort of argument sometimes arises. You might hear talk of "paradigm shifts" or perhaps allusions to technological "singularity."

In the chip world, something analogous is the argument that "Moore's Law is dead."

Perhaps there is no clear harm in the expression of the ideas. But there are clear business consequences if leaders decide "it really is different this time."

Some might argue the classic relationship between interest rates and recessions has fundamentally changed. “It’s different this time” is the argument. That might imply we do not have to worry about recessions caused by high interest rate policy.

Others might argue the historic relationships between unemployment and inflation are “different this time.” That might imply we do not need to worry about employment rates and inflation.

Consider the argument that the relationship between interest rates and recessions (higher rates lead to recessions; lower rates promote growth) has fundamentally changed. Some now argue traditional tools such as rate hikes are less effective in combating inflation or preventing recessions.

Increased government intervention and changes in central bank mandates such as a focus on full employment alongside inflation control might reduce the effectiveness of interest rate policy. 

The argument is that increased activism by central banks (quantitative easing and forward guidance as examples) has arguably altered the relationship between interest rates and economic outcomes. 

Central bank interventions might  influence economic activity and asset prices through channels beyond just interest rates.

Some economists argue that the increasing importance of financial markets has weakened the traditional relationship between interest rates and recessions. Higher interest rates might not dampen demand for loans as effectively because alternative financial instruments exist.

Others suggest that asset bubbles can inflate and burst independent of interest rate changes, potentially triggering recessions, no matter what interest rate policy might be. 

Yet others might argue that globalization might make economies more sensitive to external shocks, automation could dampen demand for labor, and aging populations could reduce aggregate consumption, all potentially operating alongside interest rate policy. 

The traditional argument is that interest rates influence aggregate demand, investment, and therefore economic cycles, and that the relationship remains intact. 

Others might argue that such “it’s different this time” thinking is associated with asset bubbles, and the fundamental relationships between supply and demand still are intact.

Likewise, we might remember to be cautious whenever we hear that a paradigm shift is happening (it might not be happening) or that a singularity is nearing or chip progress rates must decline. Altering business strategy based on incorrect assumptions can be deadly.