"AI" Investments Skew towards Infra, but Strongly Resemble "Digital Economy" or "Internet" Themes

One question lots of mass market investors are asking these days is “where can I invest to participate in artificial intelligence expected growth?” 

Ironically, the answers are nearly identical to the answers to the question “where can I invest to participate in internet-related or digital economy growth?” 

Unless you own or work for a venture capital firm, or are an accredited investor, you will not be able to invest in startups oriented around artificial intelligence, leaving you with the task of identifying existing public firms that have some plausible direct relationship to AI. 

Eventually, as has proven to be true for the internet, most firms will have some indirect relationship to the internet, but that is not so helpful in identifying candidates for investment right now. 

As is true for just about any general-purpose technology before it (steam power, railroads, the internet, electricity) and other platforms that might not always be considered GPTs (highway systems, passenger air travel, mobile communications, telephone systems), infrastructure is where investments must be made first, before the full range of use cases develops. 

So for “regular people” the domain initially will be public firms with AI infrastructure operations: the compute power to run AI software, the products required to build AI models, make inferences, create applications or supply platforms and devices to run the models and make inferences, sustain the connectivity to processing nodes, create and run the data centers, provide AI functions as a service. 

Most of these infra segments includes firms one might own for other reasons as well (dividends, revenue growth, capital appreciation, real estate investments, software or information technology, content assets, connectivity). 

So, perhaps oddly enough, “AI investments” are pretty much the same as would be expected if one were instead searching for “digital economy” or “internet” investments, with perhaps a stronger weighting towards “picks and shovels” that create or sustain the infrastructure to run apps and provide experiences. 

Analyst Cody Willard suggests an 11-layer model focused on AI infrastructure, including some private firms or open-source initiatives, but also focusing on public firms plausibly involved in creating AI infrastructure, applications and content. 

Chips, servers, data centers, cloud computing, data management, algorithms, models, internet connectivity and end user devices are perhaps the clearest examples of AI infra. But some might also include content, enterprise AI-enabled applications or advertising as layers of the AI value chain. 

source: Cody Willard, MarketWatch 

Looking at infra as 11 or more layers, that might suggest a layer one (chips) including

• Silicon (Nvidia, duh!), Intel, AMD, Qualcomm and Broadcom (add Microsoft, Alphabet, Meta and Apple to the extent they are developing their own AI chips as well)

• Silicon design services such as Cadence Design Systems, Synopsis  or Autodesk

• Application Specific Integrated Circuit (ASIC) designer such as Broadcom , Marvel, Intel, Advanced Micro Devices and Qualcomm

• Silicon intellectual property including Arm, Intel

• Semiconductor equipment such as ASML, Advanced Materials, Lam Research, KLA Corp. and Teradyne

• Foundries including TSMC, Intel, Samsung, Global Foundries

• Memory (SK Hynix, Samsung, Micron, Western Digital, Seagate

• Machine Learning languages including PyTorch (open-sourced from Meta), TensorFlow (open-sourced from Google), Keras, Microsoft Cognitive Toolkit, Theano, Apache MXNet, Chainer, JAX, TensorFlow.js

Layer two might focus on servers, including:

• Server design (Dell, Hewlett-Packard Enterprises, Super Micro, IBM, Lenovo, Cisco, Oracle, Fujitsu, Quanta Cloud Technology, Inspur

• Server manufacturing including Foxconn, Flex, Jabil, Sanmina Corp., Pegatron Corp., Celestica, Wistron Corp., , Quanta Computer,, Compal Electronics, Inventec Corp..

• Distribution partners include Ingram Micro, Aero Electronics and CDW.

• Server installation services (IBM, Schneider Electric, Vertiv Holdings Co., Hewlett Packard Enterprise, Super Micro, Dell

Layer three can be viewed as data centers:

• Data center design and construction (Holder Construction, Turner Construction, Jacobs, Fluor Corporation, AECOM, Syska Hennessy Group, Corgan, Gensler, HDR, Mastek, Dycom

• Data center Infra, especially cooling (Schneider Electric, Johnson Controls, Carrier Global Corporation, Honeywell International Inc., Siemens AG, Super Micro, Dell, Hewlett-Packard Enterprises

• Electric components, including renewable energy, including Enphase Energy, Inc., Solaredge, First Solar, Tesla, Inc., SunPower Corporation, Schneider Electric, ABB Ltd., Eaton Corporation

• Electrical power suppliers (PNM Resources, NextEra Energy, Duke Energy Corporation, Dominion Energy), Southern Company, Exelon Corporation, American Electric Power Company, PG&E Corp., Consolidated Edison, Xcel Energy, Entergy Corp.

• Electric utility infra (General Electric, Siemens Energy, Mitsubishi Heavy Industries, Toshiba Corporation, Hitachi Ltd., ABB Ltd., BWX Technologies, Doosan Heavy Industries & Construction

• Raw materials such as copper, gold, plastic (oil) and silver (Freeport-McMoran, Newmont Corp., Barrick Gold Corp., Franco-Nevada Corp., Freeport-McMoRan Inc., Southern Copper Corporation, BHP Group, ExxonMobil, Chevron, ConocoPhillips

• Networking and interconnect gear (Cisco Systems, Arista Networks, Inc., Juniper Networks, Inc., Broadcom Inc., NVIDIA Corp.), F5 Networks, Extreme Networks, Inc., Dell Technologies, Marvel, Applied Optoelectronics, Viavi Solutions, MaxLinear, Emcore, Nlight

Layer four might be envisioned as the “cloud” layer, including: 

• Cloud data centers (Amazon Web Services, Microsoft Azure, Google Cloud Platform, Alibaba, Oracle, Tesla, Meta

• Data center real estate investment trusts (Equinix, Digital Realty Trust, Inc., CyrusOne Inc. (KKR), CoreSite Realty Corporation (subsidiary of American Tower), QTS Realty Trust (Blackstone)), Iron Mountain Inc., DigitalBridge

• Cloud computing as a service providers (AWS, Microsoft Azure, GCP, Oracle, Meta, Tesla, Alibaba

• Inference As A Service (NVIDIA, Amazon Web Services, Google Cloud AI Platform, Microsoft, Cloudflare, Akamai

Layer five might be viewed as the data layer, including functions such as data gathering and input, machine vision, data sources:

• Machine vision including Tesla, Rockwell Automation, Zebra, Cognex Corp., Keyence Corp., OMRON Corp., Basler AG, Teledyne Technologies, ISRA VISION AG

• Consumer data sources (shopping, other behavior including Meta, ByteDance, Google, Apple, Amazon, Snap, Pinterest, Yelp, Tencent, Reddit, Etsy,, Wayfair, Walmart

• Financial data (Apple Pay, Google Pay, JPMorgan Chase & Co., Visa Inc., Mastercard Inc., Discover Financial Services, PayPal Holdings, Inc., Square, Inc., Robinhood Markets, Inc., Morgan Stanley, The Charles Schwab Corp., Fair Isaac Corp., TransUnion, Equifax Inc.

• Location, travel data (Apple Inc., Alphabet Inc., Verizon Communications Inc., AT&T Inc., T-Mobile US, Inc., Uber Technologies, Inc., Lyft, Inc., Expedia, Tripadvisor

• Enterprise Data (ServiceNow, Apple, Salesforce.com, Inc., Oracle Corp., Microsoft Corp., Alphabet Inc., Dropbox, Inc., Box, Inc., Workday, Inc., AutoDesk, Adobe, Dassault Systèmes, PTC Inc., Ansys, Inc., Trimble Inc., Siemens AG, AVEVA Group plc, Bentley Systems, Inc..

• Content (Disney, Sony, Spotify, Netflix, Warner Brothers, Paramount, New York Times, Fox, Simon & Shuster, Random House

• Data Management (Amazon Web Services, Alphabet Inc., Microsoft Corp., Snowflake Inc, Datadog, MongoDB, Inc., Oracle Corp., Confluent, Inc., Broadcom Inc., Alteryx, Inc., International Business Machines Corp., Cisco Systems, Inc. 

Layer six is the algorithm and model layer:

• Algorithms (OpenAI, Google Deepmind, Tesla AI, Meta Labs

• Large language models (OpenAI, Google, Microsoft, Anthropic, Perplexity) and training

• Libraries: Hugging Face

Layer seven is the applications layer:

• Generative AI as an app (ChatGPT, Gemini, Anthropic, xAI Grok, LLaMA, Stability AI, Mistral, Mosaic, Amazon

• Apps using LLM (Microsoft Copilot & Github, Office365, Google Workspace, Duolingo, Alexa, Siri, Spotify, Palantir, Autodesk, Unity

• Data analysis (Snowflake, Oracle, Datadog, AWS, Google, Azure

• Content Creation (The Walt Disney Company, Netflix, Inc., Electronic Arts Inc., Warner Bros. Discovery, Inc., Paramount Global, Sony Group Corp. , Comcast Corp. , Activision Blizzard, Inc. (Microsoft), Electronic Arts, Take-Two Interactive Software, Inc., Spotify Technology S.A., Lions Gate Entertainment Corp, Fox

• Cybersecurity (Palo Alto Networks, Fortinet, , CrowdStrike Holdings,, Zscaler, , Check Point Software Technologies Ltd. , CyberArk Software Ltd., Okta, Inc., FireEye, Inc.

Layer eight might be edge networking:

• Hardware and servers (Intel, AMD, NVIDIA, Dell, HPE)

• Content Delivery Networks (Cloudflare, Akamai, Fastly, AWS, GCP, Azure)

Layer nine might be advertising:

• Venues (Meta, Google, Amazon, Snapchat, Pinterest)

• Ad placement and services (The Trade Desk, Unity, Applovin)

Layer 10 might be networking:

• Tower infra (Crown Castle International Corp., American Tower, SBAC)

• Access providers (Starlink, Verizon Communications, AT&T, Lumen Technologies, Charter Communications, T-Mobile, Comcast, Comtech, ViaSat, Iridium Communications,  Gogo

Layer 11 includes end user devices:

• Consumer devices including Apple iPhone, iPad, Mac, PCs, Google Pixel phones, Meta Glasses, Lenovo PCs

• Enterprise devices such as robots (Tesla Optimus, Mitsubishi, Kawasaki, Epson, Universal Robots, Omron, Yaskawa Electric, Fanuc, Kuka, Denso, ABB

• AI machinery (tractors, cranes, containers, and boats made by Caterpillar, John Deere, Trimble)

• AI satellites and spacecraft: SpaceX, Rocket Lab, Intuitive Machines, Optimus by Tesla)

• AI Drones by AeroVironment, Lockheed Martin, Boeing, Northrop Grumman

Some of us might argue that mass market investors should view virtually all these assets as categories we’d consider for other reasons, despite their AI potential, as it might be some time before AI revenues are material. 

In Willard’s 11-layer model, some of us might consider much of layer seven and virtually all of layers eight through 11 as being part of the broader computing and internet value chains, and not specifically powered by AI potential. 

And parts of layers one through six would be required to support modern computing, even if AI did not exist. The point is that the AI value chain overlaps substantially with the internet value chain. With a few specific exceptions, Nvidia being the primary example, virtually all the other assets in the developing AI value chain would also be candidates for ownership as part of the internet value chain. 

AI Productivity Impact Might be Hard to Identify, in Most Cases

Don't be surprised if artificial intelligence, after a few more years, seems not to have very much positive impact on productivity overall, though it almost certainly will have had observable impact in some industries and use cases.

Information technology innovations tend not to register too much gain for entire economies, as a rule. Economy-wide productivity is the result of all sorts of changes and inputs, and improvements on that score are relatively modest, taken in total.

Keep in mind that total productivity changes include effects from all sources, not just information technology. So unless you believe IT was solely responsible for total productivity change since 1970, the actual impact of IT arguably is rather slight, perhaps on the order of 0.5 percent up to about 1.5 percent per year, maximum. 

And those years would include the impact of personal computers, the internet and cloud computing, to name a few important information technology advances. 

That noted, some industries seem to boost productivity more than others, and perhaps we can argue that IT is responsible, in part.

Perhaps there is no contradiction between low historical total factor annual productivity gains and high expected generative artificial intelligence revenue impact; productivity impact or profit impact for some firms in some industries. 

Industry Sector (NAICS Code)	Description	Percent Change in Labor Productivity (1977-2022)
Total Nonfarm Business (All)	Covers all industries except agriculture, government, and private households	1.0%
GoodsProducing Industries (1133)	Includes mining, construction, and manufacturing	0.4%
Manufacturing (3133)	Factory production of goods	0.5%
Construction (23)	Building, renovation, and maintenance of structures	1.2%
Mining (21)	Extraction of minerals and natural resources	2.3%
ServiceProviding Industries (4892)	Covers a wide range of service businesses	1.4%
Wholesale Trade (42)	Selling goods to businesses in bulk	1.2%
Retail Trade (4445)	Selling goods directly to consumers	0.4%
Transportation and Warehousing (4849)	Moving people and goods	2.1%
Information (51)	Publishing, broadcasting, and telecommunications	2.5%
Financial Activities (52)	Banking, insurance, and real estate	1.2%
Professional and Business Services (5456)	Legal, accounting, consulting, and scientific services	2.0%
Education and Health Services (6162)	Schools, hospitals, and other social services	1.3%
Leisure and Hospitality (7172)	Accommodation, food services, and entertainment	3.9%
Other Services (8189)	Repair shops, personal care services, and religious organizations	0.8%

On the other hand, some forecasts of higher impact for some firms in some industries are not necessarily incompatible with the “all industries” trends for productivity improvements. The best firms in the industries most able to use GenAI might well wring more benefit from the technology. 

In fact, that might tend to be the case for the best and worst firms in almost any industry. 

Also, cumulative productivity gains over a period of years will of course be higher than single year gains. 

Revenue gains in excess of 10 percent for some companies in some industries over a multiyear period are conceivable, even if single-year gains are in single digits or less. 

Industry	Potential Impact	Source, Forecast
Manufacturing	Increased product design efficiency and innovation  Improved production line optimization  Reduced waste and defects	McKinsey & Company: 20% to 40% productivity gains by 2030 PwC: Up to $3.7 trillion global GDP impact in manufacturing by 2030
Retail and  Ecommerce	Personalized marketing and promotions  Enhanced customer experience (chatbots, product recommendations)  Optimized pricing and inventory management	J.P. Morgan: Up to 10% revenue growth for retailers by 2030 Accenture: Up to $1 trillion in annual revenue growth for retailers by 2035
Financial Services	Fraud detection and risk management  Algorithmic trading and portfolio management  Personalized financial advice and wealth management	Goldman Sachs: Up to $1.2 trillion in annual cost savings for financial institutions McKinsey: Up to $200 billion in annual revenue growth for wealth management by 2030
Healthcare	Drug discovery and development  Personalized medicine and treatment plans  Improved medical imaging analysis	PWC: Up to $150 billion in annual savings in the US healthcare system McKinsey: Up to $6 trillion in global healthcare productivity gains by 2030
Media & Entertainment	Content creation (music, scripts, video)  Personalized content recommendations  Streamlined content production workflows	Bain & Company: Up to 10% productivity gains in media content creation by 2030

The point, though, is that big numbers predicted for applied GenAI have to be understood in context. Total-economy gains will be far smaller than many expect, even if some firms, in some industries, will show higher revenue growth; profit rates or productivity gains. 

Can Decentralized AI Succeed?

Decentralized artificial intelligence refers to a different approach to architecting AI models, analogous in some ways to the “Web3” effort to create a decentralized web that is not controlled or led by a few large firms. Some might also liken decentralized AI to the concept of “peer-to-peer” computing, which likewise uses a distributed network of physical computation. 

Decentralized AI aims to distribute the physical basis of the AI platform across a network of computers, and in some cases also distribute the logical platform of data sources upon which the models are built.

Decentralized AI proponents believe it promotes transparency in how AI models are trained and used and might reduce bias if diverse datasets from various sources are used. This might not be a particularly strong advantage, as all models are trained on a wide variety of data sources. 

Some believe there are data privacy or user monetization advantages. Federated learning techniques might allow users to keep their data private while still contributing to the training of AI models.

By distributing AI models across a network, it should be more difficult for any single entity to control or censor them. Lots of startups, not all of which will survive, have emerged. 

Ocean Protocol (OCEAN) provides a decentralized marketplace for data and AI models. Users can buy, sell, and share data securely while ensuring data privacy. 

SingularityNET also aims to create a global marketplace for AI services and tools built on a blockchain platform. Fetch is another blockchain-based AI effort. 

Numerai is  building a decentralized hedge fund powered by a community of AI developers and data scientists. 

Federated learning and Secure Multi-Party Computation (SMPC) underpins Oasis Labs; Federated AI and a group within Intel working on federated learning techniques and privacy-preserving AI.

Firms including Filecoin (storage network); Livepeer (decentralized video streaming) and Theta Network (decentralized video delivery) are building more application or use-case-specific forms of decentralized AI infrastructure.

Personal AI aims to build personal assistants. Vana wants to create a way for Reddit users to contribute their data. MyShell wants to create personalized chatbots. 

As always, it is hard to tell whether the alternative approaches will work at scale. In fact, the historical development of computing suggests centralized approaches have won. Some might view the shift to personal computers as a form of decentralization, but without limiting the emergence of a few dominant platforms. 

Likewise, distributed email, peer-to-peer music sharing networks were forms of decentralization. But cloud computing swung the pendulum back to centralization. Blockchain is presently the platform most believe could affect decentralization efforts. 

But how many of us actually believe the “rule of three” will not eventually emerge for generative AI and other AI models? Eventually, markets operating at scale seem always led by a small number of firms. 

“A stable competitive market never has more than three significant competitors, the largest of which has no more than four times the market share of the smallest,” BCG founder Bruce Henderson said in 1976. 

"A stable competitive market never has more than three significant competitors, the largest of which has no more than four times the market share of the smallest,” Henderson argued. Sometimes known as “the rule of three,” he argued that stable and competitive industries will have no more than three significant competitors, with market share ratios around 4:2:1.

Whether decentralized AI can succeed, in that sense, is the question.