AI "Do Something, Now" Advice Will Mostly Lead to Irrelevant Outcomes

Whenever an important new technology arrives, it gets hyped, and the bigger the possible transformation, the greater the hype. And that is likely the case for artificial intelligence as well. “Act now, or fall behind,” the argument will be. 

The issue is that a truly-important technology is misunderstood, and therefore will tend to be deployed, early on, in suboptimal ways. Think of the way the early multimedia web featured firms posting their brochures online. The new is viewed through the lens of the old. 

That tends to be the case for all general-purpose technologies, from electricity to the internet and now possibly artificial intelligence. The impulse to “do something” will be overwhelming for many firms in many industries, even when the outcomes will turn out to be more or less negligible, as important as they might seem for any particular use case. 

Yes, we will be able to do existing tasks faster, with less labor, and maybe even “better” in some ways. But the real value will come later as we learn to remake whole processes. 

The initial use case for the internal combustion engine was to pump water out of mines. But the greatest value of the ICE was its impact on transportation and mobility. Though street lighting was among the first obvious uses of electricity, its greatest impact now is as a platform for all sorts of appliances, sensors and portable and mobile devices, communications and computing. 

Consider a study of 792 banking sector decision makers surveyed by Forrester in 50 countries and 12,500 consumers surveyed by IPSOS in 14 countries suggests there is a big gap between banks and their customer perceptions about “value.” 

In fact, 46 percent of consumer respondents said they were open to ditching their current provider for a new company if it offered the personalized services they want. In an age of hyper-connectivity, hyper-personalization is needed, the report argues. 

Nearly four out of ten customers now have an online bank, which 36 percent consult with at least once a day, the report published by Sopra Steria says. 

Mobile applications and websites are becoming the primary channels of exchange for 58 percent of respondents, and only 25 percent of customers say that they contact their advisor as their first means of communication.

That is part of the general “be digital” advice executives are given in virtually every industry. But it always is the outcomes that matter, despite the level of activity. 

Consider the value reaped by cloud computing. 

“A stunning 95 percent of European companies in our recent survey say they’re capturing value from cloud, and more than one in three say they intend to have more than half of their workloads on cloud,” say McKinsey consultants Bernardo Betley, associate partner; Hana Dib, associate partner; Bjørnar Jensen,  senior partner and Bernhard Mühlreiter, partner. That’s the good news. 

The bad news? “The vast majority of the value companies have captured, for example, remains in isolated pockets and at subscale,” the consultants also say. 

Some of that might be caused by the way European companies (the subjects of the study) have implemented cloud computing. 

“The focus of their cloud efforts, for example, has been disproportionately on improvements to IT, which generate lower rates of value than improvements to business operations,” McKinsey says. 

Somewhat oddly, “most companies (71 percent) measure it (benefits) in IT operational improvements, 66 percent in IT cost savings, and 63 percent in number of applications on cloud,” McKinsey says. “Only about one in three European companies, however, monitors non-IT outcomes, such as cost savings outside IT (37 percent) or new revenue generation (32 percent).”

That might seem odd for many observers, since those outcomes (cost savings or revenue enhancement) might seem the obvious way to measure outcomes. “Our research and experience are clear that about two-thirds of the potential value of cloud comes from revenue uplift and cost savings in business operations,” McKinsey says. 

Study Title/Author	Methodology	Findings
"The Business Value of Cloud Computing" by McKinsey & Company (Report) / 2019	Surveyed over 1,500 executives globally	Found that companies with a clear cloud strategy focused on business outcomes achieved 3x the return on investment (ROI) compared to those with a technology-centric approach.
"Cloud ROI: Why Businesses Need a Strategic Approach" by Forrester Research (Report) / 2021	Analyzed data from cloud adoption projects	Concluded that companies with a well-defined cloud strategy focused on business goals like agility and innovation saw a 20% increase in revenue growth compared to those with a tactical, workload-centric approach.
"Unlocking the Economic Advantage of Cloud Computing" by Accenture (Report) / 2022	Examined the impact of cloud on various industries	Identified that companies using cloud to drive innovation and customer experience saw a 15% increase in customer satisfaction and a 10% improvement in operational efficiency.
"Cloud Adoption and Firm Performance: A Meta-Analysis" by Florian Schreibert et al. (Journal Article) /2020	Analyzed 42 prior academic studies on cloud adoption	Concluded that while workload shifts can lead to some performance improvements, the most significant benefits are seen when cloud adoption focuses on strategic goals like cost reduction, efficiency, and innovation.
"The Cloud Dividend: How Businesses Benefit from Cloud Computing" by Accenture (Report) /2022	Analyzed financial data from over 10,000 companies	Identified a strong correlation between effective cloud adoption strategies focused on business outcomes and improved financial performance.

“Compared to U.S. companies, about five times more European companies are still pursuing an IT-led cloud migration, with significant emphasis on lifting and shifting existing workloads,” the consultants say. 

The point is that lots of advice about “doing something now” and “moving faster” with artificial intelligence is not going to produce measurable and useful outcomes. It will amount to mere activity. That always seems to be the case for important new technologies. 

That noted, another set of outcomes should be mentioned. Eventually, as firms and industries learn to use the new GPTs effectively, clear outcomes will be obtained. That is the good news. 

The bad news is that the value of the innovations will accrue to most firms in each industry, so sustainable long-term advantage will fail to be gotten. Important new technologies will rise to the level of “table stakes,” essentially forcing firms to invest to keep up with their competitors, and not to gain an advantage over them. 

In the meantime, lots of capital, information technology effort and time will essentially be wasted applying AI where it actually does not produce outcomes that matter. 

As during the turn of the century “dotcom” mania, every firm tried to be an “internet-first” firm. And many of those efforts simply fizzled. “We’re on the internet” or “we use the internet” failed to produce clear outcomes for most firms in most industries. 

Most firms selling in retail stores tried to add “internet” capabilities, while many new firms tried to operate “online only” in retail. Few actually produced material changes in market leadership, as did Amazon. 

Only a few later (after decades had passed) mastered the sharing economy by harnessing smartphones (with internet capability) to the process of public transportation (think Uber or Lyft), creating ridesharing, or peer-to-peer short-term accommodations (think Airbnb). 

Eventually, banks will learn to use AI in ways that materially affect customer experience, as they did with automated teller machines or online banking. 

But many early efforts will fail, in part because our present understanding of how to use AI in ways that matter is incomplete. We’re still at the stage of posting brochures online. We have yet to master the ability to “do things” online because we mostly will try to automate existing processes. 

Later, we’ll figure out how to remaster processes. But that will take some time.

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.

Can AI Make Social Media More Pleasant?

Many of us would say we avoid social media because of the amount of content that seems impolite, not respectful; immature or worse. Perhaps no amount of content moderation is going to stop some people from behaving in ways that are rude; uncivil and lacking in grace. -

Among the ways generative artificial intelligence could help reduce the amount of hostile and uncivil social media content, Generative AI models can be trained to identify patterns of language commonly used in hateful, abusive, or harassing content. This can help flag such posts for further human review by moderators.

The caveat is that some people seem to believe ideas themselves are inherently abusive, or “violent” or “threatening” when they might arguably simply reflect a difference of opinion. So “protection” for some might seem to be censorship by others. 

Assuming that sort of bias can be largely avoided (a big “if”), then perhaps AI can go beyond simple keyword matching and analyze the overall sentiment and context of a post. This can help identify nuanced forms of negativity or attacks that might bypass simpler filters, again assuming we are dealing with some agreed-upon sense of the difference between free speech; different ideas and bad behavior. 

AI might be able to analyze conversations and identify situations where a disagreement is escalating towards something more than hostility and bad manners, and the AI might suggest alternative phrasings or ways to reframe arguments to promote more civil discourse and respectful dialogue that some might call simple good manners and politeness. 

On a different level, Gen AI might be used to identify and showcase positive and constructive interactions on a platform, creating a more positive atmosphere and nudge users towards more civil behavior.

Or perhaps AI could provide users with personalized prompts to encourage them to reconsider potentially offensive language before posting.

Of course, value judgments always are involved. Some might consider certain subjects or keywords “offensive,” while others might consider those subjects or keywords merely descriptive. And historical context might matter as well. Some ideas or words might have been historically common in the past, but considered inappropriate in a modern context. 

Language also can be nuanced, and sarcasm or humor might be misinterpreted by AI. Comedians, almost by definition, make fun of lots of things. 

And there's a delicate balance between filtering harmful content and stifling free speech. Just because some people do not like some ideas does not mean they are “hate speech” or somehow “violence” or “hostility.”