Showing posts sorted by date for query up the stack. Sort by relevance Show all posts
Showing posts sorted by date for query up the stack. Sort by relevance Show all posts

Tuesday, October 14, 2025

How Big is AI Market? Depends on Your Assumptions

At the moment it is possible to generate an almost-arbitrary “market opportunity” for artificial intelligence, for a number of reasons, including notably the likelihood (virtual certainty) that AI will be embedded into most existing products and services. 


source: Business Insider 


In practice, equity analysts merge hardware, cloud infrastructure, core software, and services into what can be called the “AI technology stack.” That’s the complete infrastructure required to use AI features. Generally, those revenues are about half of total market estimates. 


The other half reflects vertical applications and functional deployments that integrate AI into specific economic sectors, industries and processes. 


Of course, we wind up double counting revenue if we tally “AI” revenues as including all apps, products and services that “use AI” in their value proposition, and then separately enumerate the value of those activities (software of all sorts, automated vehicles, search, social media, e-commerce, for example). 


Revenue Bucket

Representative Products / Activities Included

Key Analysts & Forecast Examples (2025–2035)

Share or Growth Focus by Analysts

Infrastructure Hardware

GPUs, accelerators, AI servers, high-bandwidth memory, edge AI chips, and networking equipment supporting AI compute

Goldman Sachs, Fortune Business Insights, Grand View Research

Base for most AI models; hardware CAGR ~28–30%, driven by hyperscalers (NVIDIA, AMD, Intel)

Cloud and AI-as-a-Service (AIaaS)

AI compute instances, APIs, training & inference services (AWS Bedrock, Azure OpenAI Service, Google Vertex AI)

McKinsey, Fortune Business Insights

70%+ of enterprise AI deployments run on cloud AI environments by 2030

Core Software / Platforms

Machine learning frameworks (TensorFlow, PyTorch), data labeling, MLOps tools, AI model management

IDC, Precedence Research, Grand View Research

Software accounts for ~35–48% of total revenue in 2025

Generative AI Platforms

LLMs, diffusion models, text/image/video generation tools (OpenAI GPT, Anthropic Claude, Midjourney, Runway)

Bloomberg Intelligence, Fortune Business Insights

Estimated $1.3 trillion addressable market by 2033, fastest-growing segment

Professional & Managed Services

Consulting, integration, model fine-tuning, ethical AI audits, and enterprise deployment services

McKinsey, Deloitte, PwC

Among fastest-growing components (CAGR >35%) as firms seek integration expertise

Cybersecurity & Risk AI

AI threat detection, fraud monitoring, anomaly detection, predictive risk analysis

Grand View Research, FinRofca

Included under “function” in future AI workflows; fastest CAGRs in enterprise use cases

Industry-Specific Applications (Vertical AI)

Applied AI in healthcare, BFSI, retail, automotive, manufacturing, law, agriculture

Grand View Research, Precedence Research

Analysts model vertical AI revenues separately (Healthcare, BFSI, Retail largest)

Embedded / On-device AI

Consumer and industrial IoT devices with local inference (smart glasses, phones, vehicles)

Grand View Research (Meta, Apple examples)

Growth tied to edge AI adoption; enables privacy-driven processing

AI Tools for Business Functions

HR tech, marketing, supply chain, operations, finance, customer service (chatbots, copilot-type agents)

Fortune Business Insights, Gartner

Functional AI makes up ~20–25% of total AI revenue by 2030

Quantum AI & Neuromorphic AI (emerging)

Quantum-accelerated AI algorithms, brain-like computing hardware for future models

Fortune Business Insights, McKinsey Technology Outlook

Described as post-2030 “third wave” AI enabler, with gradual commercial integration


Friday, October 10, 2025

OpenAI as an App Provider?

What is the future of enterprise software as artificial intelligence continues to advance, now perhaps shifting into additional roles within enterprise software value chains?


The proximate cause is OpenAI making a direct foray into the application software market, perhaps shifting OpenAI from model provider to a direct competitor in applications including customer relationship management, marketing automation, and sales enablement.


OpenAI now offers its own suite of SaaS applications, including the "Inbound Sales Assistant" and the "GTM Assistant" (Go-To-Market Assistant). Other tools covering front office, middle office, and back office software categories are coming, supporting sales enablement, inbound marketing assistance, customer support, product analytics and finance applications.


All of those efforts, and others certain to come, are part of the evolution of generative AI from chatbot to agent


The key issue is how well OpenAI's AI models might enable entities to "build their own custom CRM" or integrate AI into existing CRM systems.


At a high level, this is an example of OpenAI moving into additional roles across a value chain, or “up the stack” in terms of functions. 


Such “AI-native” enterprise software obviously poses a threat to current enterprise software leaders. 


source: Bain

 

At a high level, some argue that, at some point, it might not be necessary to use a specific application at all to accomplish a business task. Think of the concept as AI becoming the "gateway to business knowledge." 


It is possible the enterprise software industry is in a shift from traditional software applications to a model where AI agents handle routine, end-to-end tasks autonomously, essentially bypassing the need to use specific enterprise software for such purposes. 


Instead of opening an HR system to file a vacation request, you tell your agent: "Book vacation Oct 5–10, notify my manager, update the team calendar, and reassign tasks." The agent does it all, with no app switching or forms to fill out. 


Among other practical impacts, such mechanisms call into question the traditional license-based enterprise software models, in terms of magnitude if not role elimination. 


source: Bain


Whether there is an AI “financial bubble” or not, the reason for the investment is obvious. AI might be the most-impactful new technology since the internet, with equally-disruptive effects on many industries and firms. 


Enterprise software is but one example of the process at work. 


Thursday, October 2, 2025

Will Agentic AI Disrupt Enterprise Software? Maybe the Better Question is "How Much?"

What is the future of enterprise software as artificial intelligence continues to advance, now perhaps shifting into additional roles within enterprise software value chains?


The proximate cause is OpenAI making a direct foray into the application software market, perhaps shifting OpenAI from model provider to a direct competitor in applications including customer relationship management, marketing automation, and sales enablement.


OpenAI now offers its own suite of SaaS applications, including the "Inbound Sales Assistant" and the "GTM Assistant" (Go-To-Market Assistant). Other tools covering front office, middle office, and back office software categories are coming, supporting sales enablement, inbound marketing assistance, customer support, product analytics and finance applications.


All of those efforts, and others certain to come, are part of the evolution of generative AI from chatbot to agent. 


The key issue is how well OpenAI's AI models might enable entities to "build their own custom CRM" or integrate AI into existing CRM systems.


At a high level, this is an example of OpenAI moving into additional roles across a value chain, or “up the stack” in terms of functions. 


Such “AI-native” enterprise software obviously poses a threat to current enterprise software leaders. 


source: Bain

 

At a high level, some argue that, at some point, it might not be necessary to use a specific application at all to accomplish a business task. Think of the concept as AI becoming the "gateway to business knowledge."


It is highly possible the enterprise software industry is in a shift from traditional software applications to a model where AI agents handle routine, end-to-end tasks autonomously, essentially bypassing the need to use specific enterprise software for such purposes. 


Among other practical impacts, such mechanisms call into question the traditional license-based enterprise software models, in terms of magnitude if not role elimination. 


source: Bain


Whether there is an AI “financial bubble” or not, the reason for the investment is obvious. AI might be the most-impactful new technology since the internet, with equally-disruptive effects on many industries and firms. 


Enterprise software is but one example of the process at work.


Sunday, July 20, 2025

Antitrust Law is Becoming More Subjective: Google Search is a Case in Point

Like everybody else who follows Alphabet, browsers and search, I have trouble figuring out what the judicial system will force Alphabet to do about its “monopoly” in the search business. Among the possible remedies is a forced divestiture of Google Chrome (the browser), among other possibilities. 


That remedy might seem odd in view of the defined problem being a monopoly in “search,” not browser market share. In fact, regulators might have a hard time making the case that “browser monopoly market share” actually matters, in a strict sense, since browser use is “free to use.” 


And, of course, traditional antitrust only applies to products that have an actual price, though the line began to blur when the U.S. government took action against the bundling of the Microsoft operating system with Internet Explorer. 


Even if the browser was essentially a feature, not a “product,” the monopoly danger was seen to exist in the combination of the operating system with the browser. 


So some argue that the remedy in the Google search case has to involve some remedy involving “something else,” as use of search also is “without charge” to the user. 


So the “remedies” now center around “doing something” to a product, or products, that are offered to consumers without charge. In other words, we now are dealing with allegations of “consumer harm” where the “harm” cannot be quantified, as the products are offered free of consumer charge. 


Traditionally, antitrust law has focused on “consumer harm” in the form of  higher prices, reduced output, or diminished quality, though the latter two criteria are subjective and cannot be measured. 


So we wind up with ideas that are basically subjective. The alleged monopoly in search is said to reduce innovation on Google’s part. Or, perhaps, Google search could suffer from quality issues, as it might have little incentive to innovate. 


If consumers "pay" for free services with their data and attention to ads, perhaps that is among the effects of the “monopoly.”


Others might argue that commercial agreements, such as Google paying Apple to be the default search engine on Safari, harms the competitive process.


Others might say the harm falls on advertisers who have to pay higher prices, and therefore incorporate those costs into consumer prices. 


All of those elements might seem logical, but virtually all are subjective or indirect metrics. As always, one has to define “the market” to make a claim of monopolization. 


What are the boundaries of  a market for a "free" product? Is Google Search competing with other search engines, or with social media platforms or apps that offer direct access to information or something else. It is not clear. 


The point is that the Google search monopoly allegation pushes the boundaries of several antitrust concepts. 


Digital platforms often operate in "two-sided" or "multi-sided" markets, connecting different groups (users and advertisers, drivers and riders). The complexity lies in how harm on one side (higher ad prices) impacts the other ("free" users) and how market power is assessed across these interconnected groups. That is quite a bit more subjective than the traditional test of higher consumer prices. 


Also, the arguments against Google often center on its role as a "gatekeeper" to the internet and how it "forecloses" opportunities for competitors through its widespread defaults and exclusive agreements. This avenue does not rely on consumer harm, but rather supposed harm to other competitors. 


Rather than higher consumer prices, which cannot be demonstrated directly, the court focuses on more subjective issues, such as whether dominant firms stifle innovation in the market. 


So we wind up in the novel realm of remedies for problems where consumer harm really cannot be demonstrated in any direct form. So even the remedies involve behavioral or structural changes for products with no actual consumer price. 


The belief is that, although Chrome generates no direct revenue, it is an important distribution channel for other Google services: search, Gmail, YouTube, and Google Drive. If a structural remedy is sought (forcing Google to divest the Chrome browser, for example), it would seem to involve something other than the actual “problem” of search monopoly. 


Divesting Chrome would presumably disrupt this seamless integration, potentially reducing traffic to Google’s other services.


Element

Chrome’s Role

Value to Google Search

Default Search Engine

Chrome sets Google Search as the default engine for most users.

Drives billions of queries and reinforces Google’s search market share.

Data Collection

Chrome tracks user behavior (URLs, clicks, performance, device info).

Enhances ad targeting and search ranking precision.

Search Integration

Omnibox merges the URL bar and search bar.

Increases frequency of search queries, even when users intend direct access.

Cross-product Reinforcement

Chrome integrates well with Google services (Gmail, Docs, YouTube, etc.).

Keeps users in the Google ecosystem, raising switching costs.

User Scale

World's most-used browser (65%+ global share).

Provides a massive funnel of user traffic into Google Search.


Without control over Chrome, Alphabet could lose the ability to set Google Search as the default engine or promote its AI products like Gemini.


Chrome is estimated to contribute significantly to Alphabet’s advertising revenue by driving 35 percent of Google’s search revenue, by driving search activity, enabling data harvesting, ad targeting and therefore ad sales. 


The issue is whether the divestiture of Chrome necessarily destroys that value chain. Commercial agreements could be struck allowing Alphabet access to the data that allows the rest of the stack and ecosystem to function. 


Indeed, perhaps no new owner would be in position to create as robust a revenue model as Alphabet is able to manage. 


Many of us use Google search because we believe it is the best engine. It seems unclear how many of us would switch away from Google no matter what happens to Chrome the browser. 


It would be logical to expect a hit to Alphabet’s equity value if such a development occurs. But some might well argue some of that already is baked into Alphabet’s valuation. There is a reason Alphabet has the lowest price-earnings ratio of the “Magnificent Seven” stocks, for example. 


In the end, we might not know how much impact a Chrome browser divestiture might have on Alphabet. Some might point to pressure on search advertising, which hasn’t been seen to date, but could well happen if large language models disrupt search revenue models. 


The impact on other products in the ecosystem might likewise be hard to pinpoint. Lots of us might already conclude that Gmail, YouTube and other products in the Alphabet ecosystem are preferred and would still be preferred, whether Alphabet owns the Chrome browser or not. 


Some of us would guess that some immediate equity market impact would happen, but that hit would be erased over time. Alphabet obviously has contingency plans and would obviously innovate in other areas. 


Beyond all that, the principles of antitrust for products that “have no price” are being tested. And the tests are increasingly subjective.


Friday, June 20, 2025

A World Where "Answers" are the Issue, Not "Search" Results

The replacement of traditional search with language model “answers” shifts the internet from a link-based content ecosystem to a world where traffic is less critical than relevance and authority. 


Reduced organic traffic is already happening. 


Study/Source

Content Provider

Timeframe

Reported Traffic Decline

Notes/Findings

LinkedIn (Poffel, 2025) 1

HubSpot (Marketing blog)

Mar 2023–Jan 2025, Google AI/SGE

75% decrease (24.4M → 6.1M)

Drop attributed to AI-generated answers reducing user clicks; debate over Google updates vs AI.


Chegg (Education Q&A)

2024–2025, Google AI Overviews

34% decrease (5.6M → 3.7M)

Chegg sued Google, alleging AI Overviews use their content to keep users on Google.


Stack Overflow (Programming Q&A)

2024–2025, AI tools (ChatGPT, Copilot)

Significant decline (unspecified)

Developers get instant answers from AI, reducing visits to Stack Overflow.


Informational sites (various)

SGE early tests

18–64% decrease

Especially for "easily answerable" informational queries.

The Hoth (via MarketingEdge) 2

General publishers (featured in AI Overview)

Post-May 2024, Google AI Overviews

8.9% average drop

Sites not featured: 2.6% drop. Some small publishers: up to 70% decline (Bloomberg report).

Forbes (2025) 3

Industry-wide

Google AI Overviews

15–64% decline

60% of searches now yield zero clicks; top links pushed down, reducing click-through rates.

Bain & Company (2025) 4

General consumer search

2025, AI summaries in search

15–25% decrease

80% of users rely on AI summaries for 40%+ of searches; 60% of searches end without a click.

BrightEdge, SurferSEO, Conductor (via WhistlerBillboards) 5

Mail Online (news), general sites, bloggers

March–May 2025, Google AI Overviews

Mail Online: 56% CTR drop; SurferSEO: 34.5% CTR drop for position 1; Conductor: up to 60% traffic drop

Fashion, travel, DIY, cooking, tech review, and health bloggers report up to 70% traffic loss.

Ahrefs (via WhistlerBillboards) 5

Small recipe and health bloggers

2024–2025, Google AI Overviews

Up to 65% of top-page traffic lost

“How to” and “what is” queries especially impacted.

Chegg (via WhistlerBillboards) 5

Chegg (Education Q&A)

Jan 2024–Jan 2025

49% decline in non-subscriber traffic

Attributed to AI Overviews.


Websites dependent on high-volume, low-depth traffic arguably are at risk, as the chatbots can aggregate this information without linking back.


On the other hand, language models prioritize content that is authoritative, unique, or contextually rich. That doesn’t always or necessarily mean a citation, but might be the necessary precondition. 


The models likely will favor content from established experts or primary sources as well.


So content creators may need to optimize for being "noticed" and referenced by language models,  rather than ranking high in search results.


Yes, Follow the Data. Even if it Does Not Fit Your Agenda

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