Fear Versus Greed: Electricity Transformed into Value (Bitcoin) and Insights (Inferences)

“Fear and greed” notoriously are drivers of equity market sentiment and that is clear in the yo-yo behavior surrounding artificial intelligence equities recently. The fear is that AI investment levels are a bubble, overinvestment that will ultimately not pay off. 

The greed flows from the belief that AI is a transformative new technology that will create new winners and losers in the broader economy. 

A likely third position is that AI is not a bubble on the order of the do-com mania, but will produce excess investment that has to be rationalized, eventually, as all great new technology waves have done so. 

Optimists might agree with Mara Holdings CEO Frederick Thiel that “ electrons are the new oil,” referring to the idea that computational resources underlie the ability to wring value from AI, while the data centers that provide the computation now are dependent on access to large and affordable amounts of electricity. 

Mara believes future winners will be high-performance compute providers who have the lowest costs to produce insight per token; insight per kilowatt of power consumed, especially for enterprise private compute operations. 

As Thiel puts it, his firm, which originally ran bitcoin mining operations, now provides a high-performance computing infrastructure  that converts energy into both value (bitcoins) and intelligence (AI computing).

The broader vision for the company, as is true for many other former bitcoin miners, is "transforming energy into intelligence.” In other words, consuming electricity to power AI models and the inferences to be drawn from using those models. 

The analogy is not unlike that sometimes made to the export of alfalfa from the U.S. great plains to the Middle East. The production of the alfalfa consumes water, which becomes livestock food, which essentially also represents the value of the water consumed to grow the produce. So exporting alfalfa also is akin to exporting the water used to grow it. 

“We believe energy, not compute, really becomes the primary constraint on AI growth,” says Thiel. 

Pursuant to that belief, Mara has a venture with MPLX, formed by Marathon Petroleum Corporation, the largest petroleum refinery operator in the United States, to develop and operate multiple integrated power generation facilities and state-of-the-art data center campuses in West Texas. 

MPLX will provide long-term access to lower-cost natural gas at scale, while Mara will develop and operate on-site power generation and compute infrastructure. 

The initial capacity is expected to reach 400 megawatts with the option to expand to up to 1.5 gigawatts across three plant sites.

But Mara also is basing its business on “inference” rather than model training, as that allows it to use application specific integrated circuits (ASICs) rather than graphics processor units (GPUs), thus lowering its capital investment. 

That approach also enables use of smaller data centers and air cooling rather than the more-expensive liquid cooling. The strategy is not especially new, as others in the data center and connectivity spaces have chosen to become specialists in smaller markets (either in terms of geography or types of customers). 

But all that happens within the context of a market that is volatile. 

A positive development such as a new chip announcement, a major partnership like the AWS/OpenAI compute services deal, or strong earnings from an AI leader pushes the market into "extreme greed" territory, driving up prices quickly.

But then reports of high AI capital expenditure, delayed profitability for end-users, or a general sentiment survey warning of a "bubble" causes profit-taking and selling, plunging the market into "fear" sentiment, leading to sharp, temporary pullbacks.

Month	Major Event	Sentiment	Notable Impact
2025-01	DeepSeek Launch	Fear	Sharp drop, infrastructure risk flagged
2025-04	Trump Tariffs Threat	Fear	Market volatility spiked, quick rebound after walkback
2025-09	NVIDIA-OpenAI Chip Deal, Fed Rate Cut	Greed	Strong surge, positive sentiment returned
2025-10	Bubble Talk Surge	Fear	Renewed caution, market exhaustion warnings

The cycle resets because the fundamental belief in AI's future remains generally strong. Investors who sold out of fear often rush back in for fear of missing the next leg up (greed), making the dips short-lived and creating the current high-volatility, upward-trending cycle. 

But skepticism and hope continue to coexist and oscillate. 

Beyond the volatility, we might argue that “high-performance computing capability” has become a strategic commodity.

High-performance compute capacity arguably has become the single most critical, scarce, and expensive strategic resource in the AI industry. 

If so, long-term, multi-billion-dollar compute contracts are now a competitive necessity, resembling procurement models for essential commodities like energy or raw materials. But volatility will persist until some future time when there is much more predictability about AI investments and revenue gains. 

So nobody knows yet whether the investment boom in artificial intelligence we now see is a bubble, or not. Much conventional wisdom seems to suggest AI is a bubble, but there is disagreement. 

And if some argue it is a bubble, there remains an argument that there is a significant difference between a dot-com style bubble and an “ordinary” investment bubble associated with introduction of any major new technology. 

To be sure, for some of us, there are hints to parallels of excesses akin to the excessive dot-com investment at the turn of the century. As I was writing one startup business plan, I was told “there’s plenty of money, make it bigger.” 

As it turned out, “this time is different” and admonitions that some of us “did not get it” were wrong. Economics was not different and normal business logic was not suspended. 

But some might note that there are important differences between AI investment and dot-com startup investment. Back then, many bets were placed on small firms with no actual revenue. 

Today, it is the cash flow rich, profitable hyperscalers that dominate much of the activity. Investment burdens are real, but so are immense cash flows and profits to support that investment. 

And by some financial metrics, valuations do not seem as stretched as they were in the dot-com era, though everyone agrees equity market valuations are high, at the moment. 

We also can’t tell yet what impact artificial intelligence might have on productivity and economic growth, much less future revenues for industries and firms. 

And that might be crucial to the argument that there actually is not an investment bubble; that there are real financial and economic upsides to be reaped; new products and industries to be created. 

There is some thinking by economists that AI impact could be greater than electricity and at least as important and positive as information technology in general. 

General-Purpose Technology	Primary Timeframe of Peak Impact	Estimated Annual Productivity Boost (Peak Rate)	Macro-Level Impact Metric
Steam Engine	Mid-19th Century (Decades after invention)	0.2% - 0.3%	Contribution to annual TFP* or Labor Productivity Growth
Electrification	1920s - 1940s (30+ years after initial adoption)	~0.4% - 0.5%	Contribution to annual TFP or Labor Productivity Growth
Information Technology (IT) / Computers	Mid-1990s - Early 2000s	~1.0% - 1.5%	Acceleration in annual Labor Productivity Growth (U.S.)
Artificial Intelligence (AI) (Current Forecasts)	Early 2030s (7–15 years after GenAI breakthrough)	1.0% - 1.5%	Projected increase in annual Labor Productivity Growth over 10 years

Study/Source	Projection Focus	Estimated Gain (Over Baseline)	Caveats
Goldman Sachs (2023)	Macroeconomic Forecast (Global/U.S.)	7% increase in Global GDP over 10 years; 1.5 ppt annual U.S. labor productivity growth	Highly optimistic, assuming rapid adoption and task automation.
McKinsey Global Institute (2023)	Economic Potential of Generative AI	$2.6 to $4.4 Trillion added annually to the global economy.	Based on value from 63 specific use cases across business functions.
Acemoglu (MIT)	Conservative Macroeconomic Model	0.7% increase in TFP  over 10 years (U.S. economy).	More modest, based on historical adoption rates and cost-benefit analysis of task automation.
Brynjolfsson et al. (Micro Studies)	Firm/Task-Level Productivity	10% - 40% increase in productivity for tasks like coding, customer service, and professional writing.	These are early, firm-level gains, which historically take time to translate into aggregate macro statistics.

Each of us has to make a call: bubble or not; big bubble or only “normal” overinvestment?

Where AWS Sees Value in the AI Stack, What it Means for Investors

Andy Jassy’s recent letter to shareholders provides a way of thinking about where artificial intelligence startups will be created; where functional objectives can lead to new company revenue streams and how the value chain will develop. 

Jassy talks about bottom, middle and application layers of AI. Using the software stack as an analogy, this corresponds to infra, middleware, app layers. 

The bottom layer includes both hardware and software: AI foundation models (generative AI, for example);  the computing infra required to train models and generate inferences and the software that makes it easier to build these models. 

Jassy points out that virtually all the leading models have been trained on Nvidia chips, but that customers “have asked us to push the envelope on price-performance for AI chips.”

So Amazon Web Services has built custom AI training chips (Trainium) and inference chips (Inferentia). Those chips are used by Anthropic, for example. Other users include include Airbnb, Hugging Face, Qualtrics, Ricoh and Snap.

Customers building their own models must organize and fine-tune data, build scalable and efficient training infrastructure, and then deploy models at scale in a low latency, cost-efficient manner.

Amazon SageMaker is a managed, end-to-end service for preparing their data, managing experiments, training models faster, lowering inference latency and improving developer productivity, Jassy says. 

At a broader level, all that implies opportunities for rival graphics processor units, acceleration chips, generative AI models and AI “as a service” businesses, 

The middle layer is for customers seeking to leverage an existing model, customize it with their own data, and leverage a leading cloud provider’s security and features to build a GenAI application as a managed service, Jassy says. 

We might also liken the process of “rendering” to the middle layer as well. In computer graphics, rendering is the creation of 2D images from 3D models. In audio production, rendering refers to the process of creating a complete audio file from multiple different tracks. 

In video production, rendering (editing, formatting) might refer to the processes of creating a final version of the product, adding visual effects or formatting for specific delivery formats (resolution, frame rate). 

Amazon Bedrock is an example of this layer, including “Guardrails: to safeguard what questions applications will answer), “Knowledge Bases,” as well as “Agents” to complete multi-step tasks) and “Fine-Tuning” to keep teaching and refining models.

Customers using Bedrock include ADP, Amdocs, Bridgewater Associates, Broadridge, Clariant, Dana-Farber Cancer Institute, Delta Air Lines, Druva, Genesys, Genomics England, GoDaddy, Intuit, KT, Lonely Planet, LexisNexis, Netsmart, Perplexity AI, Pfizer, PGA TOUR, Ricoh, Rocket Companies, and Siemens, Jassy says. 

AI “as a service” presents one set of opportunities, but every commercially-viable AI model will require this sort of middle-layer support as well, often sourced from third parties. 

The top layer of the AI stack is the application layer. 

For Amazon that includes shopping assistants, smarter versions of Alexa, advertising, customer service and seller services, as well as coding support apps to write, debug, test and implement code. Such apps might also support moving platforms from older to newer versions, conducting queries across multiple data repositories, summarizing data, conducting conversations and taking actions as assistants.

We should already see investment shifting from generative AI models to applications (ways to use the models to solve business problems or conduct consumer operations and tasks. Some examples include apps aimed at industry verticals, horizontal functions such as customer service or coding, fraud detection, healthcare diagnostics or supply chain optimization. 

As always is the case for general-purpose technologies, early investment goes into creating infrastructure. Later investment broadens to create applications and use cases across multiple industries and functions. 

Startups will be the field for private equity firms, institutional investors and accredited investors. Most of the opportunities for consumer investors will come in the form of publicly-traded firms with some plausible involvement in bottom, middle and app layers (infra, middleware, end user and retail supplier use cases). 

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.