Monday, December 29, 2025

Ironically, Home Owner Solar Power Damages Universal Service

A shift of consumer electrical service charges from “usage” to “a connection fee,” as happened in the telecom industry, will upset traditional thinking about how to support an effective and yet affordable electrical grid under changing usage patterns. 


Some will argue that such a switch is harmful to lower-income customers. Others will argue higher electricity costs are equally harmful to lower-income customers. As always, there are trade-offs. 


Assume a traditional utility rate that might include charges for fixed and variable costs (generation and customer usage) of:

  • Fixed costs: $100/month (embedded)

  • Energy rate: $0.20/kWh

  • Average bill (500 kWh): $100


But self generation even by consumer customers changes the business model, to say nothing of large business customer self generation. 


If, for example, a home owner installs a solar system, the grid-delivered electrical usage bill could fall to $20 to $30 a month. But the common costs of maintaining the grid do not change. Eventually, that causes a capital recovery problem. 


So assume pricing changes to include a heftier “access to the grid” fee, with lower usage fees, possibly including a grid access fee of perhaps $60 per month; usage of $20 per month at $0.08/kWh. 


The customer still pays $80 per month to remain connected to the grid and the utility does not go out of business. 


The telecom industry had to make this explicit shift over the last couple of decades as usage of fixed networks dropped dramatically, replaced by use of mobile networks. At the same time, demand for the core “voice service” changed as internet access became the anchor product on the fixed network. 


So today the fixed network is supported more by fixed “access” charges than “usage.” And even at that, policymakers argue that fixed cost recovery mechanisms are insufficient. 


Still, the advantages are that explicit revenue mechanisms to cover shared and fixed costs are available, even as more customers remove themselves from the fixed network. 


Subsidies are targeted and transparent and cross-subsidies are policy choices, not accidents. Even on the replacement mobile networks, the widespread use of flat fee access, with “unlimited” data usage, national voice calls and text messages, show the reliance on “access to the network” pricing, rather than “minutes of use” or “bytes consumed” usage models. 


The implications for the electricity network, as more customers move to self generation, is rather obvious. The sunk costs of the grid must be paid for, irrespective of individual customer usage. 


“Access to the network” becomes the “product,” rather than usage.


What’s really happening is a decoupling of value from volume, something that also happens in other infrastructure contexts. 


The grid’s value is optionality and insurance, but it’s priced like a commodity pipeline. Distributed generation exposes that mismatch.


As for the argument that access fees hurt low-income customers, consider today’s situation, where solar power benefits homeowners with the means to self generate; living in sunnier climates. 


Renters, those without capital or physical means to generate their own electricity and people living in less-sunny climes are disadvantaged. 


Access fees help ameliorate such problems, while still protecting an electrical utility’s ability to build and maintain universal access networks under conditions where “best customers” are creating their own substitutes. 


Access fees, rather than usage, now dominate telecom service fees. People actually pay for ability to use the networks, not the amount of usage of those networks, which once was the case. 


If electrical energy networks must have a “universal service” character, then we also have models for ensuring such access for lower-income customers. We use subsidies.


Electricity Business Can Learn from Telecom Evolution

Oddly enough, local electricity generation by businesses and homeowners exposes a key problem for electricity supplier economics. Traditional pricing assumes energy consumption is equal to grid usage. 


But distributed generation breaks that assumption. Essentially, customers remove themselves, at least partially, from the system, but retain the optionality of using the grid for reliability, backup, and peak load balancing. 


But fixed costs stay embedded in the price of per-kiloWatt hour charges, so rates will rise as sales fall. At the same time, new demand driven by high-performance computing and associated data centers increases the need for new investments in transmission infrastructure as well as generation, increasing the fixed costs. 


The basic problem is a combination of high fixed costs; low marginal costs per additional kWh and the impact on ability to cover fixed costs when demand is reduced by local generation. 


Since fixed costs do not decline proportionally with local generation, all remaining sales must cover more fixed cost per kWh consumed. 


This pushes per-kWh rates upward for customers who still rely heavily on the grid. 


But the network still must be designed for peak load, sized to serve customers when solar output drops (night, winter, clouds). So self-generation reduces energy delivered, not the need for the grid.


Share of Customers with On-Site Generation

Utility Retail Sales (as % of original)

Fixed Cost Recovery per kWh

Average Retail Rate Impact for Non-Solar Customers

0% (baseline)

100%

$0.10/kWh

Baseline

10%

93%

$0.108/kWh

+8%

25%

82%

$0.122/kWh

+22%

40%

68%

$0.147/kWh

+47%

60%

52%

$0.192/kWh

+92%


What’s really happening is a decoupling of value from volume, something that also happens in other infrastructure contexts. 


The grid’s value is optionality and insurance, but it’s priced like a commodity pipeline. Distributed generation exposes that mismatch.


So what might be done to fix this problem? Fixed monthly connection charges are one way of “socializing” grid costs. Time-of-use pricing and demand charges also can help. But as with mobile and fixed telecom networks, “access” to the network might be more important than usage charges. 


So reframing the product might be conceptually necessary. The “product” electrical utilities sell is reliability, capacity, and load balancing, not just energy. 


Energy is a commodity that is part of the service, but grid access becomes the actual “product.” 


Beyond all that, perhaps more explicitly cross subsidies are needed, as once was the case for communications services, where business user profits subsidized consumer usage. Perhaps business customers and self-generators must subsidize customers unable (for financial or physical reasons) to participate in self generation. 


Until pricing reflects capacity and availability, not just kWh, rising self-generation will continue to raise rates for those most dependent on the grid.


This reminds me very much of how economics of the “telecom” business changed with competition. 


Both electrical grids and telecom networks have the same core traits:

  • Extremely high fixed costs

  • Very low marginal cost per additional unit of usage

  • Peak demand, not average usage, drives capital investment

  • Universal-service expectations layered on top of commercial economics


Historically, both industries solved this with implicit cross-subsidies. But widespread technology changes and deregulation changed the telecom business model. 


Traditionally, high prices for business customers (especially long distance calling) provided the profits that allowed affordable service for consumers. 


This worked as long as high-margin users couldn’t easily bypass the network and suppliers had pricing power. 


Self generation in the electricity business has the same dynamics. When high-value customers (commercial, industrial, affluent residential) can self-generate, electricity providers lose the profits that allow them to serve mass-market customers reliant on the grid with affordable rates.


The cross-subsidy that once flowed invisibly is exposed. The analogy with telecom after deregulation, mobile substitution for fixed voice, embrace of internet protocol and reliance on internet access as a core service for the fixed network illustrate the issues. 


Dimension

Traditional Telecom Access

Electric Grid (Emerging)

Core asset

Nationwide access network

Transmission & distribution grid

Cost structure

High fixed / low marginal

High fixed / low marginal

What drives capex

Peak simultaneous usage

Peak demand & reliability

Primary pricing unit

Minutes / lines

kWh

Implicit subsidy source

Business & long-distance margins

High-usage / high-income customers

Subsidy recipient

Residential & rural users

Low-income & non-solar customers

Bypass mechanism

VoIP, wireless, OTT apps

Rooftop solar, storage, microgrids

Resulting problem

Access prices no longer cover costs

Volumetric rates no longer recover fixed costs

Regulatory response

Access charges, USF fees

Grid access charges, demand charges (emerging)

Political constraint

Universal service obligation

Universal service + decarbonization goals


The problems are similar. Neither industry can simultaneously have volume-based pricing; high fixed costs; widespread abandonment of the core network and stable rates for mass-market customers. 


The telecom industry adapted by shifting its revenue model. Today,  customers do not primarily pay for minutes or megabytes anymore. They pay for “access to the network.” Think of it like Wi-Fi access. One pays to be connected, not for usage (bytes consumed or time connected or bandwidth provided). 


The analogy is a mobile phone service plan offered at a flat fee per month that includes “unlimited” data usage; “unlimited” national calling and text message. 


The customer pays for the ability to use the network, not consumption in a strict sense. 


Today’s electrical energy service problem is that self generation reduced kWh sales while fixed costs remain. As rates rise to cover fixed costs borne by fewer customers, there is more incentive to defect. 


So an access-fee model more effectively recovers shared fixed costs. So self generation no longer erodes fixed cost recovery. And the grid stays healthy.


Wednesday, December 24, 2025

AI Capex Changes Meta View on Open Source

To a large extent, the huge cost of compute infrastructure for artificial intelligence is upending the economics and strategy of “open source,” especially at Meta


For the past three decades, the prevailing narrative of the technology industry has been one of "commoditizing the complement." This strategy, articulated by Joel Spolsky, founder of Stack Overflow, Trello, HASH, and Fog Creek Software (now Glitch), suggests that companies should strive to make the infrastructure surrounding their product as cheap and ubiquitous as possible to drive value toward their own unique layer. 


By making the infrastructure a commodity, the industry enabled an explosion of innovation "above" the stack, for software as a service, mobile apps and digital services, for example. 


That worked for Linux, Apache, and MySQL, which could be developed in the software realm. 


In the AI era, the "infrastructure" is no longer just code that can be shared; it is massive compute power that is quite physical and therefore much more difficult to share, much less afford without clear and direct monetization mechanisms. 


In the traditional software era, the barrier to entry was human ingenuity, not hardware access. A developer with a laptop could leverage a "LAMP" stack (Linux, Apache, MySQL, PHP/Python) to build a global platform. 


This leveled the playing field, moving the competitive battleground to the application layer includinguser experience, network effects, and business model innovation.


That doesn’t work in an era where compute hardware is essential. Not only are investors demanding a financial return on those investments, but the infrastructure itself becomes a competitive moat, essentially changing the value of the “open” strategy. 


In the past, a flourishing ecosystem where the "value" was found in what you built with the tools, rather than the tools themselves. 


The rise of large language models fundamentally alters the math. For AI, the "infrastructure" is the model itself, and building that model requires a "factory" built on graphics processing units or other accelerators. 


Unlike a software kernel that can be written once and distributed at zero marginal cost, a frontier AI model is a physical and linear manifestation of energy and silicon.


The difference is profound, a shift from "bits" to "atoms," virtual to physical. And though the outcome is as yet unsettled, it has been argued that only a handful of entities such as Microsoft, Google, Amazon, and Meta possess the balance sheets required to compete at the frontier because they can afford the physical infrastructure. 


That is less the case now that “neocloud” providers offering high-performance computing as a service now are available. 


Some might argue that, while the internet era was defined by who had the best idea, the AI era may be defined by who has the most power (electrical and financial). Again, the caveat is whether sufficient, affordable high-performance compute facilities are commercially available. 


The rise of data centers focused on high-performance computing, includingCoreWeave, Nebius, Lambda Labs and others, will tend to shift the business models for some providers from capital investment to operational expenses. 


So the infrastructure "compute moat" changes from an absolute barrier to entry to a variable cost. The point is that the ability to “own” a high-performance computing infrastructure is not the barrier it once seemed. 


And there already are signs the terrain is shifting. 


Aspect

The "Old" Moat (Pre-2024)

The "New" Moat (Late 2025)

Primary Barrier

Ownership of H100 GPU Clusters.

Proprietary "Reasoning" Data & RLHF.

Strategy

Vertical Integration (Own the DC).

Architecture Efficiency (Train for less).

Infrastructure

Proprietary Cloud (Azure/AWS).

Multi-Cloud/Agnostic (Rent where available).

Value Capture

Selling Compute / Tokens.

Selling Outcomes / Agentic Actions.

AI Referral Traffic Still 1% of Search, Overall

Artificial intelligence referral traffic might still be a minor part of total search traffic, says Conductor. AI referral traffic currently represents just over one percent of total web visits and is growing by roughly one percent each month.


As with all such statistics in rapidly-changing markets and industries, one might infer either that “it is not a big deal” or that “most of the growth lies ahead.” 


source: Conductor


Monday, December 22, 2025

AI Changes Search and Online Shopping, But Not as You Might Initially Think

Studies by Adobe of consumer behavior changing search and shopping behavior already show the impact of generative artificial intelligence.  Generative AI traffic grew 4,700 percent year over year in July 2025, for example. And though you might think that means trouble for search or online shopping, the opposite might be happening.

source: Adobe


And usage in key industry verticals likewise is skyrocketing, especially in the travel industry


AI-driven traffic to travel websites is up 17 times since July 2024. And Adobe estimates AI referrals generate 80 percent more revenue per visit than non-AI referrals.


Also, AI-driven consumers have bounce rates 45 percent lower than traditional sources. 


Adobe’s surveys suggest 29 percent of respondents are using generative AI for travel. Consumers use AI tools to:


 

source: Adobe 


Conversion rates, on the other hand, have yet to reach par with conversion rates for traditional online shopping, leading Adobe to conclude that AI still is being used more in a “research” way than as a direct shopping tool. 

 

source: Adobe


The traditional narrative that generative AI would decimate search volume by providing "instant answers" (thereby eliminating the need to click or search further) also is being challenged. Recent industry reports and financial earnings suggest that GenAI is acting as an accelerant.


Study/Source

Key Evidence & Findings

Impact on Volume/Monetization

Link

Microsoft Advertising (2024-2025)

Reported a 30% lift in aggregate CTR and 76% higher conversion rates for search journeys that include Copilot compared to traditional search.

Monetization Boost: Higher quality leads and engagement.

Microsoft Blog

Alphabet (Google) Q3 2025 Earnings

Revenue reached $102.3B (+15.9%). Notably, paid clicks and CPC both rose by 7%, driven by AI enhancements in search.

Revenue Growth: AI is "lifting monetization efficiency" rather than cannibalizing it.

Investing.com Report

BrightEdge Generative Parser (2025)

AI search visits showed double-digit month-over-month growth. AI Overview citations now overlap with organic rankings by 54.5%, up from 32%.

Volume Growth: Rapidly expanding touchpoints and user discovery.

BrightEdge Research

Exploding Topics / Semrush (2025)

Proposes that LLM-driven search will account for 75% of search revenue by 2028. Traditional organic search will shrink as a share, but total revenue will shift to LLMs.

Revenue Shift: GenAI becomes the primary revenue engine.

Exploding Topics

Forrester: Predictions 2025

90% of business buyers who used GenAI to inform large purchases ($1M+) reported positive results; younger buyers are increasing search-driven research via AI.

Engagement Boost: Increased research volume in B2B sectors.

Forrester Report

WPP: 2025 Global Ad Forecast

Projects global ad revenue growth of 8.8% in 2025, specifically identifying AI-endemic advertisers and AI platforms as a new "intelligence" revenue category.

Market Expansion: AI is "minting new types of companies selling advertising."

WPP 2025 Forecast


Some Small Business Owners Believe AI is Enabling "Do It Yourself" Alternatives That Cost Them Revenue

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