How do Computing Products Sold Close to Marginal Cost Recover Capital Investment?

Marginal cost pricing has been a common theme for many computing industry products. The concept is that retail pricing is set in relation to the cost of producing the next units, but not including amortization of any investments in infrastructure. 

Almost counter-intuitively, there are many examples of firms selling computing products at (or near) marginal cost (sometimes at prices near zero), yet still producing strong long-term capital recovery and attractive ROIC. 

That seems to defy economic logic, but it does work. How, since the investments in infrastructure still must be recovered?

Simply, the firms did not monetize the thing they priced at marginal cost, but instead “monetized what that thing made possible.”

The underlying economics, in computing, are simple: marginal cost collapses faster than average cost. So once a fixed investment is made (processor cycles; storage reads; memory access; bits transmitted; copies of apps), it is possible to price the commodity layer at marginal cost but then recover capital in a complementary scarcity layer. 

In other words, marginal cost pricing works when a supplier has something else to monetize, someplace else in the value chain or stack. 

The IBM mainframe business sold batch jobs or processor time as a service at marginal cost. But IBM recovered the cost of its invested capital other ways. It’s margins on hardware were high. Its customer lock-in was similarly high. 

And IBM was able to sell system engineering and software pertaining to its machines and ecosystem. 

Layer	Monetization
Hardware	Extremely high gross margins
Switching costs	Proprietary architectures
Integration	Services + system engineering
Software lock-in	Non-portable applications

So marginal cost pricing for compute services worked because customers could not switch platforms. So financial returns came from other elements of the platform. 

Microsoft provides another example. Copies of Windows; Office and developer tools were sold at affordable prices. But Microsoft made its profits from its operating system “monopoly;” developer lock-in; bundled distribution and version upgrades. 

Layer	Explanation
OS monopoly	Controlled application access
Ecosystem tax	Developers required Windows
Version upgrades	Periodic re-monetization
OEM bundling	Forced distribution

So Windows licenses were “cheap” relative to value delivered, with an incremental cost that was effectively near zero, but with profit margins near 90 percent. 

What Microsoft essentially monetized was its control of the “standard” for operating systems and the platform. 

Google search arguably offers an even-more-compelling case, as the product is available to users at zero cost. 

Search queries cost nothing. Neither does use of Google Maps, Gmail, Android or the Google productivity suite. 

But with its advertising monetization, Google creates a revenue model based on user attention. 

Layer	Value capture
User attention	Scarce
Intent data	Extremely scarce
Ad auctions	Competitive pricing
Data feedback loops	Increasing returns

So apps requiring lots of compute infrastructure are monetized other ways. “Compute” is not the product; audiences are. 

Amazon Web Services, it can be argued, prices core products near marginal cost (EC2 compute, S3 storage, throughput). 

Mechanism	Explanation
Scale advantage	Lowest unit cost globally
Demand aggregation	Extremely high utilization
Service layering	Databases, AI, analytics
Switching friction	Architecture dependence

So AWS monetizes risk reduction and reliability rather than compute cycles. “Trust” creates the revenue model while lock-in sustains it. 

Perhaps the best example is Open Source, which, by definition, is “free to use.”

Products such as Red Hat are sold at marginal cost (licensing) or the software itself is available at no cost. 

Scarce layer	Revenue source
Support	Enterprises pay for certainty
Certification	Compatibility guarantees
Hosted services	Managed convenience
Security updates	Operational risk reduction

The Apple business model might not seem to be a case of marginal cost pricing for hardware, as such pricing is not bound by marginal cost parameters. 

On the other hand, the ecosystem software (iOS, macOS, developer tools, some cloud services) actually can be characterized as being made available at marginal cost. 

Apple recovers its infrastructure and sunk costs from hardware profit margins, ecosystem lock-in and services. 

Firm	What Sold at Marginal Cost	What Recovered Capital
IBM	Compute usage	Hardware + lock-in
Microsoft	Software copies	Platform control
Google	Search	Advertising
Amazon AWS	Compute	Scale + reliability
Red Hat	Software	Support & ops
NVIDIA	Runtime compute	Chips + ecosystem
Apple	OS + tools	Devices + services

If computing marginal cost approaches zero, then retail pricing also tends to fall to near zero, while successful firms find other places in the value chain to recover capital investment costs. 

Where might scarcity value remain?

• Time

• Trust

• Risk transfer

• Attention

• Control points

• Integration responsibility

• Physical manufacturing

• Distribution

How AI Could Affect Your Investing Strategies

If you are active as an investor, you've had to spend at least some time evaluating where and how to participate in artificial intelligence: what to buy, what to avoid, and your reasons for doing so. And some of the implications are a bit startling for our thinking about computing-related hardware and software.

Generative AI might turn some computing “principles” upside down, while sustaining others. We have in recent decades seen software produce more value than hardware. In place of asset-light software, we might see value created in greater amounts by capital-intensive physical infrastructure. 

Examples might include compute “as a service” providers; power providers; fiber networks and cooling solution providers. Returns might flow to a smaller number of suppliers able to afford the huge investments in capital-intensive, long-lived physical facilities underpinning AI compute operations. 

Asset-light software might produce less value. Contrary to the recent “software eats the world” model, AI rewards scale and capital access. 

And where value has been created by asset-light, fast-moving small teams, AI should favor larger providers with enough scale to navigate markets that are highly-regulated. 

Regulatory compliance and trust barriers will tend to protect incumbents with scale. 

Likewise, we might see a shift in acquisition value. Where merger and acquisition activity recently has been about “acquiring talent,” AI might force something of a shift to “acquiring assets.”

That might include sources of proprietary data, distribution capabilities and relationships or compute infrastructure and energy resources, rather than teams of people. So the “aqui-hire” strategy might have to be revised. 

On the other hand, generative AI might support the current value of “distribution” or direct customer relationships. Much as distribution became more important once the cost of creating content dropped (social or legacy media), so, as content creation increasingly has a marginal cost of production near zero, 

audience control captures value.

Much of the impact of computerization in general, and AI in specific, has been to emphasize value creation underpinned by scarcity, on one hand, and by scale on the other hand. This sort of “high and low” or “barbell” source of value squeezes out the middle (good but not great; too much labor to fully automate, not enough brand equity to command premium pricing). 

But, in some cases, the changes will be dramatic. Where business strategy, until recently, was to “move up the stack” from lower levels to higher, the reverse could happen, in some instances. 

Value and competitive moats might be created “down the stack” in infrastructure, rather than “up the stack” in apps. “Asset ownership” might produce more value than “asset-light” business models. 

Value also might hinge, in some cases, on better applied judgment (figuring out the better models, sources of value and sources of scarcity (data, distribution, regulatory barriers). In at least some cases, that might mean a revenue model based on outcomes or performance. 

Industry	Value Chain Role	Judgment Being Scaled	Why It Wins	Likely Monetization
Professional services (legal, accounting, consulting)	Senior advisory / opinion layer	Risk tradeoffs, precedent weighting, strategic advice	Execution automates; clients still pay for responsibility	Outcome fees, retainers, premium advisory
Healthcare	Diagnostics , treatment planning	Pattern recognition + clinical judgment	AI assists, but liability and trust anchor value	Per-decision, subscription to clinicians
Finance / Investing	Portfolio construction, risk oversight	Capital allocation under uncertainty	Alpha = judgment, not data volume	Assets under management fees, performance fees
Insurance	Underwriting, pricing	Risk selection and exclusion	Better judgment = structural margin advantage	Loss-ratio-driven profits
Cybersecurity	Threat prioritization , response	Signal vs noise discrimination	Attack volume explodes; prioritization is scarce	Platform + premium response services
Media, content	Editorial direction / curation	What matters, what to ignore	Abundance makes selection valuable	Subscriptions, sponsorships
Education	Curriculum design, assessment	What to learn, in what order, and why	Content cheap; sequencing is hard	Tuition, cohort-based pricing
Supply chain, logistics	Network design, exception handling	Tradeoffs between cost, speed, resilience	Automation fails at edge cases	Optimization-based pricing
Enterprise IT	Architecture, systems integration	Tradeoffs across cost, security, flexibility	Complexity increases with AI	Long-term contracts
Telecom / connectivity	Network planning, traffic engineering	Capacity allocation under uncertainty	AI drives demand volatility	Regulated or contract pricing
Energy, utilities	Grid management, load balancing	Reliability vs cost vs emissions	Errors are catastrophic	Regulated returns
Marketing, growth	Strategy, budget allocation	Channel mix, attribution judgment	Content automates; spend decisions don’t	Performance-based fees
E-commerce, retail	Merchandising, pricing strategy	Demand forecasting, margin tradeoffs	SKU explosion increases complexity	Margin expansion
Manufacturing	Process optimization, quality control	Yield vs throughput tradeoffs	AI reduces waste; judgment prevents failure	Cost savings share
Real estate, infrastructure	Capital allocation , siting	Location and timing decisions	Long-lived assets amplify good judgment	Asset appreciation
Regulatory, compliance	Policy interpretation, enforcement	Ambiguity resolution	Rules expand faster than clarity	Subscription + advisory