Tuesday, October 29, 2024

Generative AI: Why It is All or Nothing

Suppliers of generative artificial intelligence frontier models (advanced artificial intelligence systems that push the boundaries of what generative AI can achieve) are under scrutiny for capital investments and pathways to revenue, given the explosion in investment that started in 2023. 


Risk is very high, but so is the potential reward. And even if most contenders eventually will lose their bets, the winner is epected to dominate the market.


Some idea of the ramp up of investment can be seen in venture capital investments alone, and excluding investments by leading firms such as Google, Microsoft, Meta, Apple and Amazon, to support generative and other forms of AI. 


Year

Estimated VC Investment (Billions USD)

2020

0.2

2021

1.2

2022

2.7

2023

22.4

2024 (projected)

30+


Those figures do not include any sums spent by enterprises, software or hardware firms to create AI features, apps or platforms. Nor doe those amounts include investment by hyperscale app providers or  device firms to add AI features to their existing products. 

source: Our World in Data 


Estimating all AI capex by the hyperscale app and device providers is difficult, as many types of capex as well as operating expense (personnel, for example) are involved. Often, all we know is aggregate capex. 


Company

Time Period

AI-Related Capital Expenditure

Combined (Apple, Amazon, Meta, Microsoft, Alphabet)

Q2 2023*

$59 billion

Combined (Apple, Amazon, Meta, Microsoft, Alphabet)

2024 (forecast)

$215 billion**

* total capex, not just AI capex

** source: New York Times 


And most of the revenue tied to AI investments is indirect, often contributing to cloud computing as a service revenues, for example. 


Company

AI Model/Product

Reported Financial Outcome

Microsoft

Azure OpenAI Service

$10 billion investment in OpenAI, contributing to 27% growth in Intelligent Cloud revenue in Q4 20232

Google (Alphabet)

Various AI products

28% year-over-year increase in Google Cloud revenue in Q2 2023, partly attributed to AI offerings3

IBM

Watson AI platform

$1 billion in annual revenue from Watson and AI-related services as of 20214

Palantir

AIP (Artificial Intelligence Platform)

31% year-over-year revenue growth in Q2 2023, with AI driving new customer acquisition5


We should expect to see continuing reporting of AI-attributed revenues by firms making big AI investments, even as we have to expect an awful lot of somewhat indirect approaches to identifying that revenue growth. 


Just as important, if generative AI winds up being a “winner take all” business, as most other computing segments have been, there will be no prize for third best. 


We have already seen that pattern in many other computing markets. The leader in search has 91 percent market share. The browser leader has 65 percent share. The mobile operating system leader has 72 percent share. The U.S. ride-hailing leader has 68 percent share. 


Market

Dominant Player

Market Share

Runner-up

Market Share

Search Engines

Google

91.9%

Bing

3.0%

Desktop Browsers

Chrome

65.72%

Safari

18.22%

Mobile Browsers

Chrome

66.17%

Safari

23.28%

E-commerce

Amazon

37.8% (US)

Walmart

6.3% (US)

Video Streaming

YouTube

2.5B users

Netflix

231M subscribers

Music Streaming

Spotify

31%

Apple Music

15%

Ride-hailing (US)

Uber

68%

Lyft

32%

Cloud Services

AWS

32%

Azure

22%

Mobile OS

Android

71.8%

iOS

27.6%


So if generative AI follows that pattern, the strategic choices are “don’t play” or “play to be number one.” That implies most of the investment, in most of the firms, will be stranded or lost, eventually. But a whole ecosystem might be built around the leader.


Monday, October 28, 2024

Build Versus Buy is the Issue for Verizon Acquisition of Frontier

Verizon’s rationale for acquiring Frontier Communications, at a cost of  $20 billion, is partly strategic, partly tactical. Verizon and most other telcos face growth issues, and Frontier adds fixed network footprint, existing fiber access and other revenues, plant and equipment. 


Consider how Verizon’s fixed network compares with major competitors. 


ISP

Total Fixed Network Homes, Small Businesses Passed

AT&T

~70 million

Comcast

~60 million

Charter

~50 million

Verizon

~36 million


Verizon has the smallest fixed network footprint, so all other things being equal, the smallest share of the total home broadband market nationwide. If home broadband becomes the next big battleground for AT&T and Verizon revenue growth (on the assumption mobility market share is being taken by cable companies and T-Mobile from Verizon and At&T), then Verizon has to do something about its footprint, as it simply does not have enough ability to compete for customers across most of the Untied States for home broadband using fixed network platforms. 

And though Frontier’s customer base and geographies are heavily rural and suburban, compared to Verizon, that is characteristic of most “at scale” telco assets that might be acquisition targets for Verizon. 


Oddly enough, Verizon sold many of the assets it now plans to reacquire. In 2010, for example, Frontier Communications purchased rural operations in 27 states from Verizon, including more than seven million local access lines and 4.8 million customer lines. 


Those assets were located in Arizona, California, Idaho, Illinois, Indiana, Michigan, Nevada, North Carolina, Ohio, Oregon, South Carolina, Washington, Wisconsin and West Virginia, shown in the map below as brown areas. 


Then in 2015, Verizon sold additional assets in three states (California, Texas, Florida) to Frontier. Those assets included 3.7 million voice connections; 2.2 million broadband internet access customers, including about 1.6 million fiber optic access accounts and approximately 1.2 million video entertainment customers.


source: Verizon, Tampa Bay Business Journal 


Now Verizon is buying back the bulk of those assets. There are a couple of notable angles. First, Verizon back in the first decade of the 21st century was raising cash and shedding rural assets that did not fit well with its FiOS fiber-to-home strategy. In the intervening years, Frontier has rebuilt millions of those lines with FTTH platforms.


Also, with fixed network growth stagnant, acquiring Frontier now provides a way to boost Verizon’s own revenue growth.


For example, the acquisition adds around 7.2 million additional and already-in-place fiber passings. Verizon already has 18 million fiber passings,increasing  the fiber footprint to reach nearly 25 million homes and small businesses​. In other words, the acquisition increases current fiber passings by about 29 percent. 


There also are some millions of additional copper passings that might never be upgraded to fiber, but can generate revenue (copper internet access or voice or alarm services, for example). Today, Frontier generates about 44 percent of its total revenue from copper access facilities, some of which will eventually be upgraded to fiber, but perhaps not all. 


Frontier already has plans to add some three million more fiber passes by about 2026, for example, bringing its total fiber passings up to about 10 million. 


That suggests Frontier’s total network might pass 16 million to 17 million homes and small businesses. But assume Verizon’s primary interest is about 10 million new fiber passings. 


Frontier has estimated its cost per passing for those locations as between $1000 and $1100. Assume Verizon can also achieve that. Assume the full value of the Frontier acquisition ($20 billion) was instead spent on building new fiber plant outside of region, at a blended cost of #1050 per passing. 


That implies Verizon might be able to build perhaps 20 million new FTTH passings as an alternative, assuming all other costs (permits, pole leases or conduit access) were not material. But those costs exist, and might represent about 25 percent higher costs. 


So adjust the cost per passing for outside-of-region builds to a range of $1300 to $1400. Use a blended average of $1350. Under those circumstances, Verizon might hope to build less than 15 million locations. 


And in that scenario Verizon would not acquire the existing cash flow or other property. So one might broadly say the alternative is spending $20 billion to build up to 15 million new fiber passings over time, versus acquiring 10 million fiber passings in about a year, plus the revenue from seven million passings (with take rates around 40 percent of passings). 


Critics will say Verizon could do something else with $20 billion, to be sure, including not spending the money and not increasing its debt. But some of those same critics will decry Verizon’s lack of revenue growth as well. 


But Verizon also sees economies of scale, creating projected cost synergies of around $500 million annually by the third year. The acquisition is expected to be accretive to Verizon’s revenue, EBITDA and cash flow shortly after closing, if adding to Verizon’s debt load. 


Even if the majority of Verizon revenue is generated by mobility services, fixed network services still contribute a quarter or so of total revenues, and also are part of the cost structure for mobility services. To garner a higher share of moderate- to high-speed home broadband (perhaps in the 300 Mbps to 500 Mbps range for “moderate speed” and gigabit and multi-gigabit services as “high speed”), Verizon has to increase its footprint nationwide or regionally, outside its current fixed network footprint. 


One might make the argument that Verizon should not bother expanding its fixed network footprint, but home broadband is a relative growth area (at least in terms of growing market share). The ability to take market share from the leading cable TV firms (using fixed wireless for lower speed and fiber for higher speed accounts) clearly exists, but only if Verizon can acquire or build additional footprint outside its present core region.


And while it is possible for Verizon to cherry pick its “do it yourself” home broadband footprint outside of region, that approach does not offer immediate scale. Assuming all else works out, it might take Verizon five years to add an additional seven million or so FTTH passings outside of the current region. 


There is a value to revenue Verizon can add from day one, rather than building gradually over five years.


Here comes Large Language Model PC and Web Browser Control

Large Language Models are starting to shift capabilities from content creation to control of PC or web browser functions. 

Anthropic's updated Claude LLM gives users the option of granting the tool some control over a PC, including looking at a screen, moving a cursor, clicking buttons and typing text. 

Examples of what Claude can do include filling out forms, planning an outing, and building a website.

 

 Claude 3.5 Sonnet is the first frontier AI model to offer computer use in public beta. It goes without saying that such efforts will be subject to user concern about errors and mistakes as well as privacy and security.

Microsoft had to retreat on the Copilot+ PC Recall feature that stored a user's screen shots. Meant to help people find and remember things they've previously seen on their computer. But users seemed to dislike the privacy and security dangers. So the feature now is optional. 

Google, for its part, is said to be working on Jarvis, the next iteration of its Gemini generative AI model. Said to work with web browsers, Jarvis is said to be a tool to automate everyday web tasks such as by taking screenshots, clicking buttons or entering text. Perhaps more important, Jarvis is intended to help users make purchases, fill out forms, compile data into tables, open a series of webpages, or book flights online, for example. All those are examples of how AI can be integrated into useful common experiences for users. 

Friday, October 25, 2024

It's Okay to be Skeptical About Claimed AI Outcomes

podcast of this article


A KPMG study suggests technology, telecom and media firms are already seeing, or expecting, return on investment from artificial intelligence spending. In fact, survey respondents reported hefty revenue growth, with 38 percent of respondents suggesting AI already drove more than 10 percent of total entity revenue. 


source: KPMG


In my experience, most enterprise software buyers are quite skeptical of such claims, if intrigued. And few respondents who profess such outcomes are equally able to quantify the outcomes, when asked to do so. 


“We think this outcome can be attributed to a specific input” is one matter. Actual proof is something else altogether. And there is no shortage of reasons for respondents to make such claims. Leaders always are eventually required to justify investments, costs of those investments and attributable financial outcomes.


As the old adage goes, “nobody ever got fired for recommending we buy from IBM.” In other words, bad choices can be job-ending moves. 


As always, the assumptions are key. There is a difference between stakeholder expectations about how AI can contribute, and the actual outcomes. We expect positive outcomes or would not make the investments. But outcomes and productivity are notoriously difficult to quantify for any sort of knowledge or office work.


Neither is it easy to quantify the specific outcomes enabled by any single change a firm makes, when multiple inputs--all dynamic--might be involved. 


For example, the “Redefining TMT with AI” report talks about the benefits of AI-driven predictive network analysis, in tandem with robotic process automation to  enhance network operations and quality

of service. 


In a strict sense, there are two independent variables here: process automation and use of AI. Beyond that, KPMG consultants note that the use case involves automated scripts and algorithms; predictive models; fault prediction; alarm handling; trouble-ticket management;, configuration management; 

 customized network-level reports and workflow management. 


As many of you know, such process automation already is a feature of many operational support systems. AI should help, of course. 


But it might be hard to quantify the degree of impact. Still, the point is that ROI is created by a reduction in volume of alarms, faults and tickets; improved Mean Time to Repair and reduced downtime. I cannot think of a single OSS platform or system that fails to mention those outcomes as benefits. 


In the video content industry, the report suggests AI produces ROI by affecting the efficiency and accuracy of video dubbing (language translation) and synchronizing the dubbed dialogue with the onscreen actors’ vocal movements. The ROI then is produced by reduced production time and cost. 


Also, AI is used to “for understanding and translating complex scripts and  while supporting real-time lip-sync. Basically, in this use case AI aids the dubbing process. 


AI also is used to speed up software coding, so the ROI is based on faster development cycles, faster debugging, code quality and developer productivity. 


The issue is not AI and its ability to improve all those processes and use cases. That indeed is the attraction. Instead, the issue is that it is hard to isolate the AI contributions from the other value created by the processes AI enhances. 


Directv-Dish Merger Fails

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