Friday, March 28, 2025

AI Will Personalize Education, Learning, Research as the Internet Personalized Content

All the angst we sometimes hear notwithstanding, new technology including artificial intelligence always changes learning, teaching and research. Once upon a time the invention of printing enabled mass availability of books. Television enabled distance learning. Personal computers individualized instruction. 


The internet broke geographical barriers and made instant knowledge access possible. Smartphones mean we can learn any place, any time. AI might simply enable more personalization and customization of learning or research than has been possible, until now, in the same way that the internet has allowed personalization and customization of content for individuals. 

 

Technology

Era

Impact on Learning

Impact on Education

Impact on Research

Printing Press

15th Century

Enabled mass production of books, increasing literacy

Standardized curricula and made knowledge more widely accessible

Allowed for broader dissemination of research findings

Television & Radio

20th Century

Brought educational content to remote locations

Allowed for distance learning and public education programs

Provided a medium for academic discussions and public dissemination

Personal Computers

Late 20th Century

Allowed for interactive learning via software

Digitalized education and enabled early e-learning

Increased computational power for data analysis

Internet & Search Engines

1990s-Present

Gave instant access to global knowledge

Enabled online courses, MOOCs, and remote education

Made literature reviews and information retrieval faster

Smartphones & Mobile Learning

2000s-Present

Allowed learning anywhere, anytime

Mobile apps and microlearning increased accessibility

Enabled field research and instant data collection

Artificial Intelligence (AI)

2020s-Present

Personalized learning experiences and AI tutoring

Automated grading, chatbots for student support, and data-driven education strategies

AI accelerates data analysis, hypothesis generation, and automates research tasks

Virtual & Augmented Reality (VR/AR)

Emerging

Immersive, experiential learning (e.g., medical simulations)

Enhances classroom engagement with interactive lessons

Enables virtual labs and remote field studies

Blockchain

Emerging

Secure academic credentials and transcripts

Prevents diploma fraud and enhances credential verification

Secure, tamper-proof research records


Thursday, March 27, 2025

Cherry Picking Lumen's Consumer Fiber Business

It perhaps always is difficult to value copper access lines when considering an acquisition with the intention of upgrading those lines to fiber access. It might also be somewhat difficult to value fiber lines in neighborhoods and parts of cities, even when there is no intention to buy copper lines and upgrade them. 


Without question, though, the “upgrade” analysis is more difficult. For starters, not all lines really are candidates for upgrading. In some cases, most lines might not be candidates. In such instances, the “upgrade to fiber” business plan will hinge on a minority of lines. 


Assume that perhaps 35 percent to 45 percent  of Lumen Technologies' consumer access lines could be profitably upgraded to fiber. 


But assume the hypothetical $5.5 billion purchase price of the Lumen “consumer fiber business” by a buyer such as AT&T is reasonably accurate, and only includes the already-built fiber assets and customers. 


Without further details, we are left to wonder what assets are included, but It might be reasonable to conclude that it is a “cherry picked” set of assets not including central offices, voice infrastructure and copper lines. 


That might be because the clearest economics are already captured by the existing fiber facilities. Back in 2022 Lumen’s fiber-to-home footprint reached about 27 percent of total access lines. By some estimates it is possible that Lumen or another owner could upgrade between 35 percent to 45 percent of consumer access lines to fiber on a profitable basis. 


But by some estimates Lumen might have built most of the lines it can in markets where it would be the first fiber provider. In many cases the business case for upgrading and becoming the second fiber provider in a neighborhood might not be attractive. 


In markets where a single provider uses fiber, consumer buy rates can hit 40 percent of locations passed. In a market where Lumen is the second provider, it might only get 20 percent take rates. 


The flip side is that more than half of all Lumen’s existing copper facilities likely cannot be upgraded for economic reasons. 


And the copper-based business continues to decline. In early 2024, Lumen had perhaps  4.2 to 4.6 million consumer access lines generating revenue. By early 2025, this number is likely to have further decreased to 3.6 to four million consumer access lines used by paying customers. 


Access Line Type

Total Lines

Total Consumer Accounts

Total Consumer Access Lines

8,200,000

N/A

Fiber Lines

3,600,000

2,100,000

Copper Lines

4,600,000

1,900,000


Basically, a buyer intending to upgrade Lumen consumer lines is basing that decision on perhaps 2.9 million to 3.6 million out of 8.2 million lines, conservatively. By some estimates, Lumen might already have upgraded as many as 3.6 million lines, though that figure also includes small business lines that are routinely counted in the “mass markets” bucket. 


Perhaps there is some revenue to be generated from the copper lines, but it is a declining resource. 


Based on a $5.5 billion purchase price, that implies a per-line investment of between $1897 and $1528 for existing fiber lines, possibly not including any copper lines that are theoretically upgradeable. 


We must assume that there are two different types of potential buyers. In one camp are firms that see the potential to increase equity value by upgrading copper access facilities to fiber. In another camp are firms that primarily want the incremental revenue. The former includes firms that see eventual asset sales. The latter mostly includes operating firms in the business for the long haul. 


If we assume that Lumen would prefer to get out of the consumer mass markets business altogether, a key issue is whether the rest of the consumer business and facilities (central offices, voice infrastructure, non-upgradable lines) would be retained, spun off to another third party if possible, or bundled on a low-cost basis to a potential buyer that really just wants the fiber assets. 


It’s messy. For starters, Lumen (or any new owner acquiring the whole mass markets business) probably would continue to be viewed by regulators as a “carrier of last resort,” meaning it would have to keep offering voice services broadly and might also not be allowed to decommission the copper access network. 


An owner might argue it could use other technologies (mobile network, for example) to supply voice and lower-speed internet access service, even if it decommissioned the whole copper network. But regulators have resisted such pleas in the past. 


The point is that an acquisition of the Lumen mass markets business would be messy. The value is the fiber lines and potential boost in fiber customers. But getting those lines might also entail getting lots of copper lines that actually cannot be upgraded and have declining value. 


And if a potential acquirer only wanted the fiber for internet access and other “data” purposes, the central offices and voice infrastructure would not be very helpful. Beyond that, Lumen’s consumer fiber access lines are scattered about in some neighborhoods in many cities. There are no cities with ubiquitous fiber in place. 


Of course, it always is possible that a potential acquirer really only wants the fiber-to-home facilities that already are in place (neighborhoods), with no intent to buy copper lines and upgrade them. That’s arguably an easier business case to make, as there is not requirement for additional capital for the upgrades from copper. 


AI Scribes Produce Operational Impact, but Not Identifiable Financial Outcomes, Yet

Ambient scribes convert verbal patient-provider interactions into structured notes for clinical documentation and, eventually, medical billing. Useful, of course. The innovation saves time.  But at least one study suggests the financial impact is unclear. 


That is likely to be a recurring issue for many types of artificial intelligence features and apps. 


Peterson Health Technology 


The issue is that faster task completion, reduced human error, and streamlined workflows do not always  translate that into immediate financial gains. In fact, financial impact might be neutral to negative at first precisely because time and money has to be spent to implement the solutions. 


This perhaps is not unusual for new technology solutions. Enterprise resource planning (ERP) systems also promised efficiencies, such as the ability to generate reports faster. Still, the financial payoff wasn’t instant. As always, firms had to redesign their business processes to scale up. 


Likewise, cloud computing cut information technology overhead and boosted agility, but early adoption did not always lead to an immediate financial outcome.


Perhaps AI operational wins are the low-hanging fruit. Customer service chatbots reduce call center workload, but revenue metrics do not automatically improve. 


A 2023 Gartner report suggested 60 percent of AI projects improve process metrics, but only 30 percent show clear financial uplift within a year, for example. 


Study/Source

Operational Improvement

Potential Revenue/Profit Impact

Gartner Research

IT leaders in mature AI organizations identify business metrics early and use clear attribution strategies3

Not immediately quantifiable

Cleveland Clinic

AI used to predict patient influx and optimize staffing3

Indirect impact on efficiency, not direct revenue

Axis Bank

AI assistant handles up to 15% of calls3

Operational efficiency gain, but no direct profit mentioned

McKinsey Survey

About half of C-suite leaders describe AI initiatives as still developing or expanding9

Returns not meeting initial expectations

MIT Economics Paper

Predicted TFP gains from AI over next 10 years less than 0.55%2

Modest macroeconomic effects, not immediate profit

Virtasant Report

AI enables 24/7 customer service and reduces errors in complex processes3

Long-term strategic benefits, not immediate profit

Wednesday, March 26, 2025

Invest in AI Value Chain or Just Ignore it?

Though institutional and retail investors alike are investigating opportunities in artificial intelligence outside the venture capital area, iit often is hard to target such investment since the AI value chain is so broad. To some extent, the advice to invest in firms and sectors you would choose for other reasons seems logical enough, especially if one believes AI will eventually affect every industry. 


Of course, some segments of the AI value chain have been more obvious direct participants. Nvidia’s graphics processing units, for example, have driven its equity value in recent years. The “AI as a service” providers including AWS, Azure and Google also have been early investor “infrastructure” or “picks and shovels” favorites. 


Value Chain Segment

Estimated Value Creation (%)

Example Public Companies

Explanation

AI Chips & Hardware

~15%

Nvidia, Intel, AMD

Specialized semiconductors and hardware accelerators power AI computations. Their performance improvements drive overall AI efficiency and capability.

Cloud & Data Infrastructure

~30%

Amazon (AWS), Microsoft (Azure), Alphabet (Google Cloud)

Scalable computing, storage, and data processing services form the backbone of AI deployment. They enable vast data collection and processing at scale.

Core AI Software & Tools

~20%

Alphabet (TensorFlow), Microsoft (Cognitive Services), IBM (Watson)

AI frameworks, libraries, and algorithm platforms that underpin model development, training, and deployment across various industries.

AI Applications & End-User Solutions

~35%

Salesforce (Einstein), Adobe (Sensei), Meta Platforms, Tesla

Direct consumer and enterprise applications—ranging from recommendation engines and personalized marketing to autonomous vehicles—that capture final user value.


But the issue is that if AI is a general-purpose technology, it will affect virtually all industries. So one way of looking at investment is simply to put money where you think growth or dividends are, depending on one's investing perspective, and ignore AI, assuming it will become part of the value of every product and every industry.


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