How Might Generative AI Affect Software Profit Margins?

Are there  profit margin analogies for generative artificial intelligence as it is applied by software firms?  In other words, will generative AI profit margins prove to be equivalent to, or lower than, or higher than, profit margins in other industries that have faced new technology eras?

Generally speaking, media and connectivity firm profit margins have declined in the transition from pre-1990 to post-1990 periods when digital technology replaced analog. The exception, at least so far, is mobile service, which by most measures is more profitable today than in the analog era. 

As always, there are multiple possible driving forces, ranging from competition to deregulation to the impact of internet product substitution. 

 

Industry	Analog Era (Pre-1990s)	Digital Era (Post-1990s)
Newspapers	High (e.g., 25-30%)	Lower (e.g., 10-15%)
Magazines	High (e.g., 25-30%)	Lower (e.g., 10-15%)
Television (Broadcast)	High (e.g., 30-40%)	Lower (e.g., 15-25%)
Radio	High (e.g., 25-30%)	Lower (e.g., 15-25%)
Telecom (Landline)	High (e.g., 30-40%)	Lower (e.g., 15-25%)
Mobile Services	Emerging (e.g., 10-20%)	High (e.g., 25-35%)

Some might argue that productivity gains--and corresponding impact on profit margins--was at one level for software firms in the mainframe and then personal computing era; perhaps a different level in the cloud computing (software as a service) era and might change again in the AI era of software. 

Others are not so sure, but most might agree that hardware margins, which historically have trended downward over time, might continue to see margin erosion over time, particularly as large end users build their own chips for processing acceleration. 

source: MostlyMetrics, The Information, Meritech Capital 

But it remains unclear how AI will affect profit margins for software firms, but some would note that margins for both hardware and software have remained relatively unchanged for nearly 40 years. 

Right now, the issue might be we have no experience curve for AI operations and value. 

Era	Hardware Firms	Software Firms	AI/Cloud Firms
Mainframe (1960s-1980s)	40-50%	70-80%	N/A
PC (1980s-2000s)	20-30%	80-85%	N/A
Cloud Computing (2000s-2020s)	20-30%	70-80%	70-80% (SaaS)
AI Era (2020s-Present)	20-30%	70-80%	50-60%

Some will argue profit margins for generative AI service providers will fall over time, based largely on large capital investments and scarce evidence yet of robust revenue models. That might be the case, though. 

Software margins have been relatively consistent despite the move from mainframe to PC platforms; onboard to cloud processing. We cannot yet say how margins might change with AI, over time. 

The issue is whether AI products retain software margin characteristics or eventually resemble margin trends for content, connectivity and computing hardware. 

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Amazon Says it Can Quantify Some Generative AI Outcomes; Target Not so Much

According to Andy Jassy, Amazon CEO, Amazon Q, Amazon’s generative artificial intelligence assistant for software development, has had a clear impact on Java upgrades. 

‘The average time to upgrade an application to Java 17 plummeted from what’s typically 50 developer-days to just a few hours,” said Jassy. “We estimate this has saved us the equivalent of 4,500 developer-years of work.”

“And, our developers shipped 79% of the auto-generated code reviews without any additional changes,” he noted. 

Beyond that, “the upgrades have enhanced security and reduced infrastructure costs, providing an estimated $260 million in annualized efficiency gains,” Jassy said. 

That is a good example of the expectation that GenAI will in many initial cases--perhaps most cases--be used to support ongoing business practices. 

While arguably helpful, are hard to quantify in ways other than “productivity” improvements, such as doing things faster. Amazon just happens to be able to apply GenAI in a way that is quantifiable, in this instance. 

Most firms will not have such outcome-oriented results. 

“Earlier this year, we integrated GenAI into the handheld devices in our stores, providing our team with rapid access to best practice documentation and the ability to quickly receive straightforward responses to common questions like, how do I sign a guest up for a Target Circle card, and how do I restart the cash register in the event of a power outage,” said Michael Fiddelke, Target CFO and COO. “Since the full chain rollout of this new tool, our team members have leveraged the technology more than 50,000 times, giving answers in a highly efficient average chat time of less than one minute.”

We can agree that is a productivity enhancement, but likely also agree that it is virtually impossible to correlate with financial results or operational outcomes.

"Everyone" Hates Advertising, But Wants the Value

It is commonplace these days for observers to note that if a user is not paying for a product, then the user is the product. Sometimes that is viewed as a bad thing, but there is a bargain being struck here: users get value in exchange for being subjected to advertising.

And that is a time-tested value proposition. Users and customers get free or reduced-cost products they value for less money than would otherwise be the case. 

Product	Form of Subsidy	Value for Users
Free Online Games	In-app purchases, advertisements	Free gameplay, access to new levels/characters
Free Mobile Apps	In-app purchases, advertisements	Free core functionality, additional features for purchase
Free Video Streaming Services	Subscription model, advertisements	Free access to content, ad-free viewing option
News Websites	Advertisements, paywalls	Free access to news content, in-depth articles for subscribers
Social Media Platforms	Advertisements, premium subscriptions	Free connection with others, enhanced features for paid users
Free Email Services	Advertisements, paid storage upgrades	Basic email functionality, additional storage and features for a fee
Open-Source Software	Donations, corporate sponsorships	Free access to software, potential for customization and improvement

In principle, other forms of subsidy, discounts or premium value also are common. 

Product	Value for Users
Wholesale Clubs (Costco, Sam's Club)	Bulk discounts on groceries and household goods
Streaming Services (Netflix Premium, Spotify Premium)	Ad-free viewing/listening, access to exclusive content
Gyms and Fitness Centers (Monthly Memberships)	Access to workout facilities, classes, and potentially personal training
Subscription Boxes (Beauty, Food, etc.)	Curated selection of products delivered regularly, often at a discount
Cloud Storage Services (Dropbox Plus, iCloud+)	Increased storage capacity for documents, photos, and other files
Software Subscriptions (Adobe Creative Suite, Microsoft 365)	Access to the latest software updates and features, often with cloud storage included

The point is that as much as some decry the use of advertising, sponsorships and memberships, these are simply ways of creating value for buyers, offering lower prices and discounts, free access or perceived higher value. 

First AI Fruits Will Come From AI-Enabling Existing Products

Capital investment at Alphabet, Microsoft, Apple, Amazon and Meta is up, reaching about $55 billion in the second quarter of 2024, with the expansion largely driven by investments in artificial intelligence capabilities. 

Some of that investment comes as the firms create their own generative artificial intelligence models and train them; other costs to adapt models to the existing core firm products. So the effort initially involves some mix of creating general-purpose models other enterprises can use and applying GenAI to existing products and processes.

Alphabet and Meta have been most active creating general-purpose models others can use. But all the hyperscalers are adding GenAI to their core revenue-driving products. 

For Alphabet that means grafting AI onto search, Pixel phones and the Android operating system. Microsoft initially focused on AI-enabling its productivity suite. Virtually all the firms are creating AI assistants. 

Meta is AI-enabling its social media and communication apps. Apple largely is AI-enhancing its iPhone apps and functions, while enhancing Siri. 

Amazon is applying GenAI to its shopping experience while hosting a number of leading models on Amazon Web Services. 

source: Bloomberg 

There already are clear examples of use cases across core shopping, social media, search, recommendations engine and communications and productivity suite use cases. But much more is expected. 

Still, it is worth noting that “the things that will drive the most results in 2025 and 2026 are…the ways that AI is shaping the existing products,” said Mark Zuckerberg, Meta CEO. That noted, the Meta chief also said that “we don't expect our Gen AI products to be a meaningful driver of revenue in 2024.”

Much of the value of recommendations across media consumption and shopping for example, hinges on the value (accuracy, relevance)  of the recommendations, as well as the ease of interacting with the apps. 

“Over time I'd like to see us move towards a single unified recommendation system that powers all of the content including things like people you may know, across all of our surfaces,” said Zuckerberg. 

For advertising use cases, AI should automate nearly the entire process. “Over the long term, advertisers will basically just be able to tell us a business objective and a budget, and we're going to go do the rest for them,” said Zuckerberg. 

In other cases, where general-purpose models are created, the effort is “enabling lots of people to create their own AIs,” said Zuckerberg. 

For the most part, all the AI efforts include creating ways to add AI features and functions to all existing products.

AI Gold Rush Exists Because of the "Rule of Three"

The present generative artificial intelligence "gold rush" exists for a good reason: the Rule of Three. In most instances, 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. 

The caveat is that the rule does not work as well to industries that are unstable or heavily regulated, such as investment banking; consumer electronics; some parts of the IT software and services business; life insurance and parts of the telecommunications industry.

Industry	Top Three Players (Approximate Market Share)	Rule of Three Applies?
Automotive	Toyota, Volkswagen, General Motors (~30%, ~12%, ~10%)	Generally applies
Smartphones	Apple, Samsung, Xiaomi (~40%, ~25%, ~15%)	Generally applies
Soft Drinks	Coca-Cola, PepsiCo, The Coca-Cola Company (~40%, ~25%, ~15%)	Strongly applies
Airlines	Delta Air Lines, United Airlines, American Airlines (~15%, ~14%, ~13%)	Applies with nuances due to consolidation
Fast Food	McDonald's, Yum! Brands (KFC, Pizza Hut, Taco Bell), Restaurant Brands International (Burger King, Tim Hortons) (~15%, ~14%, ~12%)	Does not apply
Cloud computing as a service	Amazon Web Services, Microsoft Azure, Google Cloud Platform (~30%, ~20%, ~15%)	Generally applies
Retail (e.g., Online Fashion)	Amazon, Inditex (Zara), H&M (~40%, ~15%, ~10%)	Generally applies

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.

So one has to assume the same pattern will emerge for frontier GenAI models as well.

In 2023 alone, some 123 artificial intelligence foundation models, the building blocks of many modern AI applications, were released. In 2024, there may well be thousands of models in use, including both the smaller number of "foundation models" that lead the market, a larger number of general-purpose generative AI models that might be important in verticals, plus the many thousands of models that have been customized for use by specific enterprises.  

Virtually nobody believes all the would-be foundation models will survive, long term. And that fuels the "gold rush" mentality: only a few foundation models are likely to emerge as eventual market leaders, as tends to be true in any market.

While application markets tend to exhibit more diversity over the long term, compared to operating systems or semiconductor chip ecosystems, a reasonable argument can be made that, over the long term, just a handful of leading foundation models will lead the market, as that is the pattern in computing in specific and almost all markets generally.

Among the dozens of large foundation models that seem to be most used are the GPT series (OpenAI); the Claude series (Anthropic); PaLM and Gemini series (Google) as well as the LLaMA series (Meta). But there also are many small language models developing that generally are designed for specific purposes. 

In healthcare, SLMs might be used for medical document analysis, patient record summarization or perhaps research. In finance, SLMs might be used for fraud detection, sentiment analysis of financial news or risk assessment.

For customer service, SLMS might be used for chatbots. The point is that where LLMs were previously required, in the future SLMs might suffice. 

SLMs will be favored because they cost less. Training and deployment are more affordable, for example, since the models do not have to be trained on much-larger datasets. In some cases, SLMs can be developed faster. 

   

SLMs will be favored for industry-specific use cases, as well.  

Because there is less processing, there also is less energy consumption. Some argue SLMs also enable more privacy.