AI Changes Value Chains in Many Ways as did Internet

What are the likely effects of generative artificial intelligence on industries over the next five to 10 years? For some of us, the answer is based on what happened when other earlier forms of applied computing happened:

• The middle collapses

• The top captures more value

• The entry-level funnel narrows.

Basically, at the industry level, benefits flow to the firms with scale, or the firms with clear specialties. “Winner takes most” is an example. And, as might be expected in any maturing market, it becomes harder for upstarts to challenge the established order. 

Think about media (including social media and search) after the internet or retail after e-commerce. A few disruptors emerged and became dominant. So the phrase “do you want to compete directly against Google or Amazon” arose. 

AI might shift value chains as well, devaluing some forms of “effort” and creating new “platform” opportunities and forms of business leverage. Information intermediaries should become new forms of “middle” position suppliers, and see their leverage decrease. 

Platforms and ecosystems should create new value, as has been the case for the internet economy before AI. And though our experience in computing in recent decades has been that infrastructure (hardware) becomes less important as a source of value, compared to software, that might shift a bit. 

Ownership or control of compute hardware (high-performance computing data centers; graphics processors and other accelerated compute chips; energy contracts) might confer new sources of “scarcity,” which produces value. 

At the firm level, there is a related process. Entities essentially stop “monetizing work” and start monetizing “judgment at scale.” That is a subtle shift, but if effort formerly led to output which then was monetized, in an AI era the role of judgment is magnified.

It is in many ways analogous to the difference between “being efficient” (doing things at lowest cost, with least effort) and “being effective” (doing the right things to create value and monetization). 

In other words, AI will collapse the cost of doing things. It might not alleviate the need to focus on what creates value and therefore monetization of effort. 

There are many implications. Value essentially moves from headcount and process prowess and resource utilization towards proprietary data ownership; brand trust or distribution control. When content or judgment is ubiquitous and plentiful, value accrues to platforms that can aggregate audiences. 

From (Declining Value)	To (Rising Value)	Why
Human effort	Human judgment	Effort is cheap; deciding what to do and whether it’s right remains scarce
Information access	Problem framing	Everyone has answers; few ask the right questions
Execution speed	Direction-setting	AI collapses time-to-execute; direction becomes the bottleneck
Labor hours	Outcome ownership	Buyers pay for results, not process
General skill	Taste and discernment	Average quality is automated; taste differentiates
Content creation	Distribution and trust	Supply explodes; attention and credibility don’t
Workflow labor	Workflow orchestration	Managing agents > doing tasks
Static expertise	Adaptive learning loops	Models change; ability to update matters more than knowledge stock
SaaS features	Embedded AI systems	Features commoditize; integrated systems compound value
Margins from inefficiency	Margins from scale and data	AI removes inefficiency arbitrage

Across all layers, winners share three traits:

• Control of scarce assets (data, distribution, regulation, capital, relationships)

• Judgment-heavy work that AI can assist but not own

• Ability to repackage labor into products

Losers share three traits:

• Work defined by execution speed, not decisions

• Value based on information scarcity

• Pricing tied directly to human hours

Dimension	People	Businesses	Industries
Primary impact	Cognitive leverage: individuals can do more with less time and training	Productivity, cost structure, and speed of execution change materially	Value chains reorganize around automation and recomposition
Who most-easily applies	High-agency individuals who know what to ask, judge outputs, and integrate results	Firms with repeatable knowledge work and clear processes	Knowledge-intensive, rules-based, and content-heavy industries
Who is most at risk	Average knowledge workers whose value was speed, recall, or routine analysis	Firms competing mainly on execution rather than insight, relationships, or assets	Industries built on scarcity of information, not assets or regulation
Skill shift	From “doing” → prompting, reviewing, synthesizing, and deciding	From staffing depth → orchestration of humans + AI	From labor intensity → capital + data + model access
Labor effects	Wage dispersion increases; top performers pull away	Fewer junior roles; flatter organizations	Employment shrinks in some roles, grows in others (AI ops, data, governance)
Cost structure	Lower cost to create, analyze, and communicate	Fixed costs fall; variable costs tied to compute and data	Marginal cost of output approaches zero in some segments
Speed of work	Drastically faster learning and iteration	Faster product cycles, decision loops, and experimentation	Shorter innovation cycles; faster competitive turnover
Barriers to entry	Lower—individuals can launch products or content solo	Lower for startups, higher for scale players with data	Lower at the front end, higher at scale and distribution
Quality distribution	Average quality rises; excellence still rare	“Good enough” becomes cheap; differentiation shifts	Commoditization at the middle; premium at the top
Power dynamics	More power to individuals with judgment and taste	More power to firms controlling data, workflows, and distribution	Platform and infrastructure providers gain outsized influence
Long-term trajectory	Humans focus on goals, values, and judgment	Firms reorganize around “AI-first” workflows	Industry boundaries blur; ecosystems replace linear chains

----------------------

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

Small business owners believe AI is costing them business, and though that perception might be anecdotal and perhaps incorrect, it is what many believe. On the other hand, the potential sources of product substitution and the ability to “do it yourself” seem clear enough. 

As helpful as generative artificial intelligence might be for tasks (get an answer), it is far more useful when the output changes to “outcomes.” 

Last year, we might have used a language model to write a paragraph or conduct a search. Increasingly,  we will tell an agent the desired outcome (such as "launch a localized marketing campaign for Japan") and the agent will execute the tasks autonomously.

In fact, the term “do it yourself” will acquire new meaning, as the “doing” often is done by the model, while the user simply specifies what is to be done.

So in a growing number of cases, the marginal cost of "expert labor" is approaching zero. For a $20 per month subscription, a user has the equivalent of a virtual legal team, a design studio, and a coding assistant.

Existing Service/Product	AI Substitution (2026)	DIY Value Proposition
Boutique Creative Agency	Multimodal Generative Agents	8K video, brand identities, ad copy.
Tax, Accounting Services	Autonomous Financial Agents	Real-time tracking of expenses and automated filing.
Junior Legal Counsel	Contract. Compliance LLMs	Drafting, risk analysis of complex legal documents.
Custom Software Dev Shops	No-Code Agent Orchestrators	"Describe" an app; AI writes the code, deploys.
Travel Agencies,  Concierge	Hyper-Personalized AI Concierge	Agents book flights, hotels, and dinners..
Language Tutors, Translators	Real-time Neural Translation	Translation in any context.
Market Research Firms	Synthetic Audience Simulation	AI replaces surveys.

Does AI Change Wide Area Networking, and, if so, How?

It might be debatable how much changes in artificial intelligence compute workloads (training versus inference operations, for example) create new and fundamental requirements for wide area connectivity, beyond the already-existing requirement for low latency and high bandwidth.

We might, at a high level, argue that model training depends on high throughput, while inference is more dependent on low latency. Similarly, we might argue that model training relies on dense, locally-connected processors inside a single building, or in a cluster of buildings, while inference can be more-widely distributed. 

Feature	Training Architecture (e.g., NVIDIA B200)	Inference Architecture (e.g., Groq LPU, Apple NPU)
Priority	Maximum Throughput (Total FLOPs)	Minimum Latency (Time to First Token)
Precision	High (FP16, FP32, BFloat16)	Low (INT8, FP8, 1-bit Ternary)
Connectivity	Scale-Up (Dense Interconnects like NVLink)	Scale-Out (Distributed edge nodes/localized)
Bottleneck	Compute Bound (Math operations)	Memory Bound (Bandwidth & I/O)

The issue is how much connectivity decisions could be affected, aside from the overall emphasis on high bandwidth and low latency that already exists to support cloud and distributed computing. 

Some will argue that model training might always require a specialized architecture optimized for really-high bandwidth. Lumen Technologies, for example, has a vested interest in making such an argument. 

And, to be sure, the shift to inference ought to move architectures and requirements from "compute-centric" (focusing on raw math speed) to "data-centric" (focusing on moving data efficiently).

Still, it remains unclear how much the fundamental architecture, focused on both high bandwidth and low latency, could be affected. Already, some would note that memory becomes more important for inference operations. 

Overall, when inference is the driver, the "network" is no longer just a pipe for moving datasets; it becomes a live extension of the AI's memory and reasoning path. And while innovations inside data centers are coming (optical connections replace electrical), it might be the case that physical media becomes an important change, not architecture.

Hollow-core fiber networks, for example, are said to offer 47 percent faster throughput than glass core fibers. 

So perhaps the most-important wide area networking change for interconnects between data centers is new physical media, rather than architectural changes as such.

AI Media Impact: More Bifurcation of High and Low; Automated and Scarce Human Content

As someone who worked for 40 years in ad-supported media, the realities of today’s business are brutal, and that was true before generative artificial intelligence, which is accelerating the underlying economic trends. 

In a nutshell, here’s the business model problem: Many media businesses are no longer primarily “storytelling organizations” but traffic monetization systems. As writers we act as storytellers. But whether that is harnessed financially can often be unclear or unworkable. 

Since advertising “cost per thousand” impressions have collapsed over the last few decades, so have media entity revenues, bringing huge cost pressures to the forefront. 

Platforms such asGoogle, Meta and X have captured distribution and pricing power, driving many former independent or smaller entities out of the market. 

So revenue per article must be less than the cost of human labor to produce that content. So automation becomes a survival move. 

Machines can often produce content abundance, especially when the content has high structure. Humans ideally produce scarcity value when lots of insight, interpretation or “meaning” are required. But generative AI is making inroads there, as well. 

The economics of content production therefore favor using machines to produce mass content at scale, when possible, while humans have the edge only where scarce, specialized or trust-critical content is involved. 

Basically, it is all about marginal cost and associated revenue upside. Investigative reporting might, in some cases, have very-high revenue potential, though it is rare. Original analyses have high production cost, and might have moderate revenue lift. 

Breaking news, data-driven news or automated summaries invariably have low revenue upside. So the choices are fairly simple: automate what does not produce reasonable amounts of revenue, reserving human roles for the more-complicated content that will be a relatively-small part of total content production. 

Content Type	Marginal Cost	Revenue Potential	Economic Role
Investigative reporting	Very high	High but rare	Brand anchor
Original analysis	High	Medium	Subscriber retention
Breaking news rewrite	Medium	Low	Traffic defense
Data-driven updates	Near zero	Low	Volume filler
Automated summaries	~zero	Very low	Search capture

Most user-generated content business models follow a somewhat-similar, but “flipped” pattern. UGC platforms follow the same economic logic (abundance versus scarcity, automation versus humans), but the roles of humans, machines, and “content” are different. 

Abundant, low-value content is automated, while scarce, high-value content is human-produced. 

But users supply the labor for abundance, instead of journalists. The platforms automate selection, amplification, and monetization, so scarcity comes from attention, status, and trust. 

That flows from the differences in media models. Media companies pay to produce content and then monetize the content. 

UGC platforms get their content for free and monetize behavior around that content. In other words, the real product is engagement, not content. So curation is more important than content supply, which is, for all intents and purposes, unlimited. Attention is the source of scarcity. 

Generative AI accelerates the content-creation processes, which becomes even more abundant and lower cost. But the algorithms still curate. So UGC platforms will optimize for watch time; shares; comments or return visits. 

So algorithms will favor emotionally activating content; identity-affirming content or controversial content. That might be likened to “commodity” news, the sort of stuff that, in a professional media context, is structured enough to be automated. 

Top UGC creators, in terms of revenue potential, often provide insights on what matters, what to ignore or how to think about something. They often focus on meaning, which is the same “scarce human” function in professional media.

Ironically, AI increases competition for attention, but raises the premium on human scarcity. That happened with journalism after the internet; music after streaming or photography after smartphones. 

Abundance also tends to make authentic human insight more valuable, not less, even if it remains rare, as surfaced by the algorithms.