Why Apple Might Not Need to "Lead" AI

As Apple gears up for the typically-important Worldwide Developers Conference, many seem uneasy about Apple’s ability to provide evidence that its artificial intelligence strategy (“Apple Intelligence” and Siri) is working. Some worry Apple started too late or is in some ways hobbled because of its emphasis on data privacy, which limits the degree to which Apple software can take advantage of cloud-based processing. 

Those concerns might ultimately be a bit harsh.

Apple, despite the growth of its services revenue, remains a hardware company driven by sales of general-purpose devices such as smartphones. And there is an argument to be made that AI’s value is highest for special-purpose embodiments, less for general-purpose devices. 

In other words, AI’s value is crucial for automated vehicles. We might argue that AI-based health monitors likewise are instances where the AI has moderately-high value. 

For general-purpose smartphones, AI obviously aids photography operations, voice interactions or personalization. But we might expect too much of AI as a value driver beyond that. The specific value of AI for personal computers is arguably even lower.

Sure, voice interfaces are helpful, but most of the location-based personalization smartphones enable is not so pronounced with PCs. One might even argue AI provides lowish value for PCs. 

The point is that Apple’s AI strategy and imperatives are different from those of Alphabet, Microsoft or Meta. There is an argument to be made that Apple primarily has to employ AI to improve the value of phone apps (camera, for example) or user interface. AI is useful, but not existential.

For Microsoft, AI enables many of its core businesses, from gaming to enterprise productivity apps. AI might not emperil its core revenue drivers, so long as it can keep up. For Meta, whose revenue is built on content and advertising, AI might be a net plus.

Amazon might benefit directly mostly through AI-powered logistics efficiencies and recommendations and personalization for its customers.

Alphabet, on the other hand, faces the possible cannibalization of its search business model by AI alternatives. So for Alphabet, AI leadership might be an existential challenge.

So there is an argument to be made that Apple does not actually have to "lead" in broader AI. In fact, that has tended to be Apple's approach to innovation in the past, in any case. It rarely is "first" to introduce a new type of product or category. It tends to "do it better." 

And some of us recall that Apple has faced many periods when it seemed threatened. Eventually, Apple has been able to surmount such challenges. 

Period	Challenges	Rebound Strategy	Outcome
Mid-1980s to 1997	- Declining Mac sales - Fierce competition from Windows PCs - Poor leadership after Steve Jobs was ousted in 1985 - Mounting losses and product confusion	- Steve Jobs returned in 1997 through the NeXT acquisition - Simplified product line - Microsoft invested $150M in Apple (1997) - Launched iMac in 1998, designed by Jony Ive	- Apple returned to profitability - iMac became a major hit - Re-established design and innovation culture
Early 2000s (2001–2003)	- Skepticism over Apple's entry into consumer electronics - Slow Mac sales - Tech bubble aftermath weakened investor confidence	- Launched iPod in 2001 - iTunes Store in 2003 revolutionized digital music - Strengthened brand loyalty	- iPod became a cultural phenomenon - Boosted revenue and brand visibility - Set stage for future devices
2007–2009 (Post-iPhone Launch)	- iPhone faced strong criticism for lacking features (e.g., no 3G, no physical keyboard) - Doubts about Apple entering the mobile phone market - 2008 financial crisis hurt tech stocks	- Rapid iteration: iPhone 3G (2008), App Store launch - Aggressive global carrier partnerships - Focus on software ecosystem	- iPhone became Apple’s flagship product - App Store created a new app economy - Massive revenue growth
2011–2013 (Post-Steve Jobs Era)	- Concerns over Apple’s innovation capacity after Jobs’ death in 2011 - Critics claimed Apple was no longer a “visionary” company - Increasing Android competition	- Strong product roadmap under Tim Cook - Continued success with iPhone, iPad, and Mac - Services revenue growth began (iCloud, App Store, etc.)	- Stock rebounded and reached new highs - Apple maintained leadership in premium devices - Cemented Cook’s leadership credibility
2015–2016 (iPhone Saturation Fears)	- Slowing iPhone growth - China market concerns - Critics questioned reliance on a single product line	- Diversification: Apple Watch, AirPods - Expansion of services (Apple Music, iCloud, App Store) - Focus on ecosystem lock-in	- Apple became world's most valuable company again - Services and wearables became major revenue contributors
2020 (COVID-19 Pandemic)	- Factory closures and supply chain disruptions - Retail stores shut down - Global economic uncertainty	- Rapid pivot to remote work culture - Launched Apple Silicon (M1 chip) in 2020 - Robust online sales strategy	- Record-breaking quarters post-pandemic - M1 chip received critical acclaim - Apple solidified vertical integration strategy

D-Day Plus 81

Some things should not be forgotten. D-Day, 81 years ago is among them. 

Could Court-Ordered End to Google Search "Exclusive Placement" Actually be Good for Google?

One of the assertions in the “United States v. Google” 2020 antitrust case against Google is that Google acts as a monopolist in paying $26 billion annually to Apple and others to be the default search app on iOS devices, for example, sharing ad revenue resulting from searches on those devices and operating systems.

Some of us might simply note that it is very easy to change a default browser or search engine, but others will point out that few users (less than five percent) actually seem to do so. Assuming Alphabet knows its business better than we casual users do, paying to be the default search engine pays off.

The issue is whether that is monopolistic behavior, if business partners and users benefit, and if users largely use Google search because they consider it the best app in the category.

So here’s the irony. If, in the penalty phase of the trial, Google is forced to stop paying for exclusive placement as the default search engine on Apple and other devices or operating systems, it might well avoid paying the ad share fees, but remain the dominant search engine, based simply on user preference for it.

Telco Role in AI or Data Monetization Seems Limited, Really

One learns over time to be skeptical about some claims repeatedly made by leaders in many industries. Consider the claim by retail telco executives that they possess all sorts of behavioral data that can be monetized. What, exactly, are those sorts of data?

Some skeptics might point out that the data is mostly about use of communication services and devices; as well as service plan preferences. Telcos sometimes claim to have demographic data, but most of that is indirect. Location data is possible, but many app providers seem to be able to generate that themselves. 

Compare that to what a Meta or Alphabet might know: browsing histories; search queries; app usage; clicks; location; social graph. 

Feature	Telco Behavioral Data	Facebook/Alphabet Behavioral Data
Data Types	Service usage, support, demographics, purchases, devices	Browsing history, search queries, app usage, clicks, social interactions, location, interests
Granularity	Often aggregated or anonymized for privacy	Highly granular, often linked to individual profiles
Personalization Potential	Limited, especially with anonymization	Very high, enables highly targeted ads and content
Use Cases	Service improvement, pricing, churn reduction, marketing	Targeted advertising, content recommendation, user profiling
Regulatory Constraints	Strict privacy regulations, especially for sensitive data	Privacy regulations, but often more flexible with consent
Data Utility	Good for trends, limited for individual insights	Excellent for both trend and individual-level insights

Skeptics might argue that telco data is rather limited as a source of value or monetization. But telco execs keep insisting what they have is valuable. Some of us would say we haven’t seen it. 

Telco data might be viewed as substantial for internal operational and strategic planning, but it is generally less powerful than the behavioral data available to digital platforms for marketing and user engagement purposes. 

By analyzing support interactions, complaint logs, and feedback, telcos might be able to identify recurring issues such as network reliability problems, billing confusion, or service dissatisfaction. Also, analytics can detect subtle patterns such as a drop in usage, frequent complaints, or late payments that can signal a customer is at risk of churn.

But even there, the analytics might only be used proactively for business accounts. I see little evidence telcos use that data to do something about potential consumer account churn. 

In some ways, the issue is similar to the potential value of artificial intelligence, where telcos are going to be users of AI, but probably not in any particularly advantaged position where it comes to being a supplier of AI products and services.

Most Professionals in Accounting, Finance, Consulting, Law Use AI at Work

A survey conducted for Intapp finds 72 percent of professionals in accounting, finance, consulting and law use artificial intelligence at work. 

source: Intapp

"Minimal" Economic Impact of AI Chatbots, Study Suggests

With the obvious caveat that investing in new technology often does not produce measurable immediate outcomes, a study of large language model economic outcomes in Denmark suggests very-slight outcomes. 

Indeed, the study authors say “AI chatbots have had no significant impact on earnings or recorded hours in any occupation.” 

The study published by the U.S. National Bureau of Economic Research involved two large-scale adoption surveys conducted in late 2023 and 2024 covering 11 occupations; 25,000 workers and 7,000 workplaces.

Productivity gains were said to be modest, with an average time savings of three percent. But the study notes that AI chatbots have created new job tasks for 8.4 percent of workers, including some who do not use the tools themselves.

Nor has there been any impact on worker earnings. “Workers overwhelmingly report no impact on earnings as of November 2024,” the study says. 

Nor do productivity gains seem to have much impact on earnings. “We estimate that only three to seven percent of workers’ productivity gains are passed through to higher earnings,” say authors Anders Humlum and Emilie Vestergaard.

“Comparing workplaces with high versus low rates of chatbot usage, we find no evidence that firms with greater adoption have experienced differential changes in total employment, wage bills, or retention of

incumbent workers,” the authors say. 

The authors also note that Denmark has institutional characteristics similar to those of the United States, with similar uptake of generative AI; how hiring and firing costs; decentralized wage bargaining and annual wage negotiations. 

The 11 occupations studied included accountants, customer support specialists, financial advisors, HR professionals, IT support specialists, journalists, legal professionals, marketing professionals, office clerks, software developers, and teachers.

The findings should not come as a surprise. The “productivity J-curve" suggests that initial investments in new technologies may temporarily suppress productivity before delivering long-term benefits.

Study	Technology Examined	Lag Time Observed	Key Findings
McKinsey Global Institute 1,5,7	Digital technologies, AI	Years to decades	Benefits emerge after business process redesign and "creative destruction." Historical parallels (e.g., electric power) show lags of decades. Generative AI may shorten lags to months or years.
CEPR Study on French Industrialization 3	General-purpose technologies	5–10 years	Firms delayed adoption due to uncertainty, and early adopters operated technologies inefficiently. Aggregate productivity gains materialized slowly as organizational practices evolved.
Stanford CS Analysis 4,5	IT investments	2–5 years	Executives reported 5-year lags for IT payoffs. Complementary investments and learning curves delayed measurable productivity growth.
Productivity Paradox Research 5	IT, automation	2–5 years	"Productivity J-curve" observed: short-term costs offset gains until workflows adapted. Measurable aggregate gains emerged in the 2000s from 1990s IT investments.
Brynjolfsson et al. (McKinsey) 7	Generative AI	Months to a few years	Shorter lag due to existing digital infrastructure, but still requires process redesign. Early adopters see inefficiencies before optimization.