Thursday, December 11, 2025

Access Network Limitations are Not the Performance Gate, Anymore

In the communications connectivity business, mobile or fixed, “more bandwidth” is an unchallenged good. And, to be sure, higher speeds have enabled new applications. 


But it might also be fair to argue that, beyond a certain point, “more bandwidth” supplied to consumer users of mobile apps and devices has reached something of a point of diminishing returns. 


As a test, I have spent months on a fixed network connection that rarely exceeds 70 Mbps in the downstream and perhaps 7 Mbps in the upstream. 


Do I experience the difference between the symmetrical gigabit connection I am used to? Yes. But has my work or other use cases been unpleasant or unworkable? No.


And even if I detect some difference when on a PC, do I experience any difference on my mobiles when connected to Wi-Fi? Not really. 


That has been a shocking realization. 


Don’t get me wrong. I still favor higher speeds and more bandwidth. As time goes by, the cost to supply higher capacity is no greater than supplying less bandwidth once did. 


If one’s network is built to supply symmetrical 5 Gbps, then supplying lower speeds does not really cost much, if anything. The shock has been that the usefulness of today’s networks is so high that even limited bandwidth supplies high value, and does not seemingly impair the “typical” user experience. 


The caveat, of course, is that I have no need to continually upload large files, and as part of the test, have made sure there is only a single user on the test account, and have typically connected only two devices simultaneously, typically using only one device actively. 


For starters, mobile applications are designed to work efficiently even on sub-optimal network conditions (using data compression, caching, and low-resolution defaults), so absolute “highest capacity” network access is less important. 


And though data delivery matters, it often is the device’s processing speed that matters more, in terms of supplying a satisfying user experience. 


Generation

Theoretical Peak Bandwidth (Downlink)

Typical User Bandwidth

Key Applications/Use Cases Enabled

1G (1980s)

∼2.4 kbps

N/A

Analog Voice Calls (The first truly mobile phone system)

2G (1990s)

∼64 - 144 kbps (GSM/GPRS/EDGE)

∼9 - 50 kbps

Digital Voice Calls, SMS (Text Messaging), Basic MMS (Multimedia Message)

3G (Early 2000s)

∼384 kbps (Initial) up to 21 Mbps (HSPA+)

∼0.5 - 2 Mbps

Mobile Internet Browsing, Sending/Receiving Large Email Attachments, Basic Video Streaming, GPS / Location Services

4G (2010s)

∼100 Mbps (Initial LTE) up to 1 Gbps (LTE-Advanced)

∼10 - 50 Mbps

High-Definition (HD) Video Streaming, Real-Time Online Gaming, Video Conferencing, Cloud Services, App-based Ride-Sharing

5G (Late 2010s/Present)

∼1 - 10 Gbps (Peak mmWave)

∼100 - 500+ Mbps

Ultra-HD (4K+) Streaming, Ubiquitous IoT (Internet of Things), Massively Scaled AR/VR Experiences, Cloud Gaming with minimal latency, Advanced Autonomous Vehicles


At a certain point (often cited around 10-20 Mbps for high-quality video streaming), human perception limits the value of ever-increasing speed. 


Once a webpage loads in under a second or a video streams instantly, a further increase from 100 Mbps to 1 Gbps offers little discernible benefit to the typical user for those common tasks.


My point is that it is shocking how good access networks now are; how optimized the apps are and how fast the latest devices actually process. 


It has changed my evaluation of value-price relationships for access networks, both fixed and mobile. I still prefer gigabit networks. On the other hand, I am well aware that in many instances, all that bandwidth is unnecessary. 


So different value-price decisions are rational. Higher speeds remain “nice to have.” But beyond a (to me) shockingly low point, higher speeds are not necessary. 


That is quite a shift from the days when I used to pay $300 a month for a 512 kbps connection, and thought that was money well spent. But apps did not use video; streaming music was likewise unavailable; real-time apps were few and far between and devices were much more limited in terms of onboard processing. 



Year

Device Era

Typical CPU Speed (Clock Rate)

Typical RAM

Key Architectural Change/Use Case

1996

Early PDAs/Communicators (e.g., Nokia 9000)

∼20 – 33 MHz

2 – 8 MB

Transition from basic cell phone to early data/email device (Intel i386-based).

2002

Feature Phones / PDA Hybrids (e.g., Pocket PCs)

∼150 – 200 MHz

32 – 64 MB

Shift to dedicated mobile CPUs (e.g., ARM, Intel XScale); basic multimedia.

2007

First Generation Smartphones (e.g., iPhone 1, Nokia N95)

∼400 – 620 MHz (Single Core)

128 MB

Launch of modern Mobile OS (iOS, Symbian); Web browsing, early App ecosystem.

2010

Early Android / High-End Smartphones (e.g., Samsung Galaxy S)

∼1.0 GHz (Single Core)

512 MB

Standardization of the 1 GHz clock speed; Advanced mobile gaming, HD video.

2012

Multi-Core Transition (e.g., Samsung S3, iPhone 5)

∼1.0 – 1.5 GHz (Dual to Quad Core)

1 GB – 2 GB

Introduction of multi-core processors (SoC); smoother multitasking, 64-bit architecture begins.

2015

4G LTE Flagships (e.g., iPhone 6S, Samsung S6)

∼1.5 – 2.0 GHz (Quad to Octa Core)

3 GB – 4 GB

Focus on high-resolution displays (4K video recording); 64-bit architecture becomes standard.

2020

5G Flagships

∼2.5 – 3.0 GHz (Octa Core)

8 GB – 12 GB

Integration of dedicated AI/Neural Processing Units (NPUs); Advanced computational photography, early AR/VR experiences.

2025

AI/Advanced 5G

∼3.0 – 3.5 GHz (Octa Core+)

12 GB – 16 GB+

Peak clock speed growth plateaus, emphasis shifts to core count, specialized accelerators (AI/ML), and energy efficiency.


Wednesday, December 10, 2025

At Alphabet, AI Correlates with Higher Revenue

Though many of the revenue-lifting impacts of artificial intelligence arguably are indirect, as AI fuels the performance of products using it, the impact at Alphabet seems to correlate. 


AI directly contributed to accelerating Google Cloud revenue from 22 percent growth (Q3 2023) to 35 percent growth (Q3 2024). 


Operating margins improved 4.5 percentage points to 32 percent, which the company partially attributes to AI efficiencies.


Across Alphabet, some indicators are:


AI Music Revenue Models Will lean on Business-to-Business Use Cases

Automation seemingly always tends to redefine job functions and value, and artificial intelligence is unlikely to be different. Past automation efforts show the way value shifts to different functions. 


AI might be different in many ways, in part because it introduces automated content creation, such as AI-generated music, for which there could be many revenue models, perhaps in a business-to-business context more than a consumer context. 


Unlike the current model, “stars” would not be able to monetize in the form of live concerts, merchandise sales and so forth. But there are lots of other B2B possibilities, such as licensing for game developers, filmmakers and advertisers. 


Business Model

Description

Revenue Sources

Target Audience

AI Music Generation Platform

Users pay to access an AI tool to create, edit, and download unique music tracks from a text prompt or existing sound.

Subscription Tiers (monthly/annual access), Pay-Per-Generation (token/credit model), Licensing Fees (for commercial use).

Content Creators, Music Producers, Brands, Game Developers, Hobbyists.

Royalty/Sync Licensing Library

An online library of high-quality, pre-generated AI music (often human-curated/edited) sold for commercial synchronization use (sync licensing).

Perpetual Licensing Fees (one-time fee per track/project), Subscription Plans (royalty-free access to the entire catalog).

Filmmakers, Advertisers, YouTubers, Podcasters, Video Game Studios.

Direct-to-Consumer (D2C) Sales

Selling final AI-generated music tracks, albums, or exclusive loop/sample packs directly to listeners or other artists.

Digital Sales (downloadable tracks/albums), Exclusive Content (e.g., NFTs, limited edition loops), Merchandise/Fan Subscriptions.

Fans, Independent Artists, Producers looking for unique samples.

Streaming & Public Distribution

Distributing AI-generated music on major streaming platforms (Spotify, Apple Music, YouTube) and earning revenue based on play count.

Streaming Royalties (per-stream payments), Ad Revenue (from platforms like YouTube), Performance Royalties (via collection societies).

General Public/Listeners, AI Music Artists.

Enterprise/Bespoke Soundtrack Solutions

Offering customized AI music services or APIs to corporate clients for large-scale, adaptive, or functional audio needs.

SaaS/API Access Fees (integrating the AI into a client's product), Service Fees (for custom composition projects like adaptive game scores or brand jingles).

Gaming Companies, Wellness Apps (for sleep/focus music), Retail/Hospitality (background music), Major Brands.

AI Co-Pilot / Production Tools

Selling AI tools as plug-ins or features that assist human music producers with specific tasks, like generating drum patterns, chord progressions, or mastering.

Software License Sales, Subscription Access (for advanced features or cloud processing), Freemium Models (basic tools free, premium features paid).

Professional Music Producers, Sound Engineers, Composers.


Prior waves of automation likewise focused heavily on B2B use cases, including the role of bank personnel, manufacturing roles, healthcare or retail checkout, for example. 


Industry

Prior Role (Routine Task Focus)

Automation Technology

Current Role (High-Value Focus)

Retail Banking

Bank Teller (Cash handling, deposits, withdrawals)

ATM, Online/Mobile Banking, RPA (Robotic Process Automation)

Financial Advisor/Relationship Manager (Consultation, complex problem-solving, sales)

Manufacturing

Assembly Line Worker (Repetitive manual assembly, welding)

Industrial Robots, Advanced CNC Machines

Robot Technician/Engineer (Programming, maintenance, quality control, process optimization)

Retail & Checkout

Cashier (Scanning, counting change)

Self-Checkout Kiosks, Inventory Management Software

Customer Experience Associate (Assisting with complex purchases, merchandising, order fulfillment)

Legal

Paralegal/Junior Attorney (Document review, legal research)

AI-Powered Discovery Software, Natural Language Processing (NLP)

Strategic Legal Counsel (Interpreting AI results, litigation strategy, client relationship management)

Healthcare

Clerical Staff (Appointment scheduling, billing, claims processing)

Electronic Health Records (EHR), Automated Billing Systems

Patient Care Coordinator (Complex scheduling, patient advocacy, optimizing care pathways)


Access Network Limitations are Not the Performance Gate, Anymore

In the communications connectivity business, mobile or fixed, “more bandwidth” is an unchallenged good. And, to be sure, higher speeds have ...