Will AI Disrupt Internet Business Models?

One reason for studying the business implications of artificial intelligence is that we may be seeing a significant shift in the role played by near zero marginal cost in shaping feasible business and revenue models. 

We might all agree that near-zero marginal cost has been fundamental for content, social media and other app pricing and revenue models. Network effects and scale have been fundamental precisely because marginal cost has been so low. 

Low marginal cost arguably is responsible for “winner take all” market structures; the ability to support software products using advertising; the global character of markets and the importance of “time to scale.”

Marginal cost is the cost of producing one additional unit of a good or service. For non-tangible products such as software, streaming, or cloud services, this cost is often close to zero once the initial product is developed and infrastructure is in place. 

Low marginal cost means that digital businesses can grow a user base quickly without proportional increases in expenses. That is vital for businesses built on network effects, where each additional user increases the value for others (social networks, marketplaces).

Also, low marginal cost also means attackers can undercut incumbent pricing levels, often making higher margins on lower retail prices (“free” use and “freemium” models). 

Business Element	Traditional Business	Internet Business (Low Marginal Cost)
Cost per additional user	Increases with scale	Remains near zero with scale
Profit margin	Shrinks with volume	Grows with volume (after fixed costs)
Growth constraints	Physical/logistical	Virtually unlimited (digital)
Network effects	Limited	Strong, self-reinforcing

All that can enable a business model where adding more users means lower average cost per user as scale grows. That is less true for a traditional “physical” model, where cost tends to scale in a more-linear fashion. 

The implication is that AI possibly disrupts many foundational internet app, service and content models, where zero-to-low marginal cost is the economic foundation. 

The essential difference for AI-based models is that very-low marginal cost might not be so substantial.

Large language models incur non-trivial costs for training, inference, and maintenance that arguably are more linear cost drivers than we have gotten used to for many internet apps. 

Unlike traditional cloud-based internet delivered  software, where serving additional users involves negligible database or bandwidth costs, AI inference costs are directly proportional to user activity. 

On the hardware side, AI processing tasks arguably also involve data infrastructure requirements that also scale in a more-linear way. 

Traditional internet platforms might have marginal costs per user interaction estimated at $0.0001–$0.001, for example. 

AI services might have marginal costs per interaction closer to $0.01–$0.50, depending on model complexity and usage patterns.

The business model implications, if this gap does not close, is that AI model marginal costs could be higher by 100 times or more. 

And that could key implications for the value of scale and likely revenue models. Is the internet business model about to be disrupted?

Will AI Lead to Cognitive Costs "Near the Cost of Electricity?"

“The cost of intelligence should eventually converge to near the cost of electricity,” says OpenAI head Sam Altman. The economic implications could be quite significant. 

For example, the pricing of services that rely on intelligence (consulting, legal, creative) could approach the marginal cost of electricity, disrupting traditional business models. In other words, at least some cognitive tasks might become commoditized. 

Even if we might not be able to directly compare the “cost” of cognitive activity and equivalent operations conducted by an AI model, we might all agree that AI generally uses more energy and resources upfront than humans to achieve a similar single outcome, but then can scale to produce vastly more output with lower marginal cost. 

In other words, the AI advantage comes when we scale the activities. Looking at the matter in terms of water or electricity consumption, humans use relatively little energy and water in performing knowledge work, but throughput is limited and cost scales roughly linearly with quantity.

If one human produces one unit of work, then 10 units requires 10 humans. AI outperforms at scale. 

From an environmental perspective, a single human brain is “greener” than a single massive AI for one unit of task; however, to match an AI that can do one million tasks, you’d need an army of humans whose combined footprint (millions of computers, offices, and lives) might then rival or exceed the AI’s footprint.

There are other imponderables as well. At least some might speculate that we are entering an age where cognitive labor scales like software: infinite supply, zero distribution cost, and quality improving constantly. 

To be sure, the cost of employing cognitive workers is far more complicated than simple consumption of electricity and water. Still, at scale, AI impact on cognitive work does seemingly create economies of scale. 

By that logic, AI doesn’t just automate tasks; it commoditizes thinking. Of course, that looks only at cognitive input costs, not outputs. We probably are going to have to look at outcomes produced by using AI at scale, such as curing a particular disease or reducing production costs for some product. 

Still, it is shocking to ponder the economic implications of cognitive costs related to the cost of the electricity and water required to produce the models and inferences.

Bye Bye Skype

As Microsoft retires Skype in favor of Teams, it might be useful to recall just how impactful “voice over IP” services such as Skype were in dismantling the telco profit engine.

For example, looking only at revenue, in 2000 global international call revenues were in the range of $80 billion to $100 billion, with very-high profit margins. By 2020, international calling revenues had dropped to about $15 billion to $20 billion, with profit margins compressed. 

 

Decline in International Calling Revenue with VoIP Adoption (2000–2020)
Year	Est. Int'l Calling Revenue (Billion USD)	VoIP Adoption & Skype Milestones	Notes
2000	~80–100	Minimal VoIP presence; traditional PSTN dominates	High tariffs for international calls; telecom monopolies prevalent.
2003	~75–90	Skype launched; 11M users by 2004	Skype introduces free VoIP calls and low-cost PSTN calls, challenging telecom pricing.
2005	~70–85	Skype acquired by eBay ($2.6B); 54M users	VoIP gains traction; telecoms begin lowering rates to compete.
2008	~60–75	Skype grows to 405M users	Economic recession impacts telecom revenue; VoIP alternatives expand (Viber, WhatsApp emerging).
2010	~50–65	Skype disables third-party integrations; 663M users	Telecoms lose market share to VoIP; mobile data plans begin reducing VoIP dependency.
2013	~40–55	Skype-to-Skype int’l traffic up 36% (214B minutes)	TeleGeography notes VoIP capturing significant call volume; traditional revenue continues to decline.
2015	~30–45	WhatsApp, Viber, and others compete with Skype	Mobile apps erode Skype’s dominance; telecoms shift to data-driven models.
2018	~20–35	Skype daily users at 40M (2020 peak)	VoIP services saturate the market; telecom firms  focus on broadband and mobile data revenue.
2020	~15–25	Skype usage spikes 70% during COVID-19	Despite Skype’s decline to 36M daily users by 2023, VoIP remains dominant; traditional int’l calling revenue nears obsolescence.

Profit margins were an important part of the early, pre-VoIP story. Net profit margins on international voice were as high as 25 percent back in 2000. Current net margins are in the range of three percent to possibly five percent. 

In a real sense, VoIP services including Skype disrupted the telecom industry profit driver. 

Year	Domestic Long-Distance Margin (%)	International Long-Distance Margin (%)	Discussion
2000	~12–18%	~15–25%	High margins due to limited competition and high per-minute rates. Domestic margins are slightly lower than international due to local competition. Estimated from telecom sector data and peak long-distance revenue.
2001	~12–17%	~15–24%	Stable margins but early pressure from mobile and VoIP adoption. International margins are higher due to termination fees. Estimated from sector trends.
2002	~11–16%	~14–23%	Decline in domestic voice revenue began as mobile plans offered "bucket" minutes. International margins remained higher but faced VoIP competition. Estimated from sector data.
2003	~10–15%	~13–22%	Continued erosion from mobile and VoIP (e.g., Skype). International margins supported by high termination rates. Estimated from sector trends.
2004	~9–14%	~12–20%	VoIP and internet-based calling reduced costs and rates, squeezing margins. Domestic margins lower due to flat-rate plans. Estimated from sector data.
2005	~8–13%	~11–18%	Long-distance business peaked in 2000; by 2005, revenues were declining rapidly. International margins are higher due to mobile international calling demand. Estimated from sector data.
2006	~7–12%	~10–17%	Domestic margins hit by unlimited calling plans and VoIP. International margins supported by slower price erosion in mobile long-distance. Estimated from sector trends.
2007	~6–11%	~9–16%	Domestic long-distance became commoditized; international margins pressured by OTT apps (e.g., WhatsApp). Estimated from sector data.
2008	~5–10%	~8–15%	Long-distance revenues halved from 2000 peak. Economic recession and VoIP adoption further reduced margins. Estimated from sector data.
2009	~5–9%	~8–14%	Smartphone adoption and VoIP apps (e.g., Skype) eroded margins. International mobile long-distance retained higher margins. Estimated from sector trends.
2010	~4–8%	~7–13%	Domestic long-distance margins near sector average (~4.82%). International margins are higher due to termination fees and mobile demand. Estimated from sector data.
2011	~4–7%	~7–12%	Domestic margins low as carriers bundled unlimited long-distance. International margins declined due to VoIP growth. Estimated from sector trends.
2012	~3–7%	~6–11%	Domestic long-distance fully commoditized; international margins affected by outsourcing and fraud. Estimated from sector data.
2013	~3–6%	~6–10%	Mobile data surpassed voice revenue; domestic long-distance margins were minimal. International margins supported by the wholesale voice market. Estimated from sector trends.
2014	~3–6%	~5–10%	Voice over LTE and VoIP reduced standalone voice profitability. International margins pressured by low-cost VoIP providers. Estimated from sector data.
2015	~3–5%	~5–9%	Domestic long-distance margins were negligible as unlimited plans dominated. International margins declined due to OTT apps. Estimated from sector trends.
2016	~3–5%	~4–8%	Voice services bundled with data; domestic margins near zero. International margins are low but supported by wholesale carriers.
2017	~2–5%	~4–8%	Domestic long-distance margins minimal; international margins affected by grey routes and fraud. Estimated from sector trends.
2018	~2–4%	~4–7%	Domestic margins near zero as voice bundled with data plans. International margins low due to VoIP and 5G adoption. Estimated from sector data.
2019	~2–4%	~3–7%	Voice commoditization is complete; international margins slightly higher due to wholesale voice demand. Estimated from sector trends.
2020	~2–4%	~3–6%	COVID-19 increased communication demand, but voice margins remained low due to free VoIP apps. Estimated from sector data.
2021	~2–4%	~3–6%	Domestic long-distance margins negligible; international margins low but supported by enterprise demand. Estimated from sector trends.
2022	~2–4%	~3–6%	Telecom services margin ~4.82%; domestic voice margins near zero. International margins are low due to wholesale price wars. Estimated from sector data.
2023	~2–4%	~3–5%	North America wholesale voice market faced intense competition, eroding international margins. Domestic margins are negligible. Estimated from sector trends.
2024	~2–4%	~3–5%	Wholesale voice market valued at $40.26 billion in 2025, but margins low due to VoIP and 5G. Domestic margins near zero. Estimated from sector data.

Also, VoIP was not the only huge driver of a shift in consumer behavior. “Calling” became something most people did on their mobile phones. 

Fixed-network revenue dropped from $200 billion globally in 2000 to under $50 billion by 2020, while mobile revenue grew from $500 billion to $1.6 trillion, for example. U.S. telco revenues likewise shifted from fixed to mobile; legacy voice to VoIP. 

U.S. Telco Revenues 2000 to 2024
Year	Mobile Voice	PSTN Voice	VoIP
2000	10	100	1
2010	60	60	21
2020	110	20	41
2024	130	4	49