AI startups do not share one business model, and they are not automatically less scalable or less profitable than traditional software companies. The key difference for investors is that an AI product may combine reusable software with recurring costs for inference, cloud infrastructure, data preparation, model operations and human review. Those costs can change margins, capital needs and the route to defensibility. Compare the company’s actual cost to serve, retention, pricing and milestone funding needs—not its category label.
Start with the business model, not the “AI” label
Traditional software and AI software are broad categories, not uniform economic profiles. A conventional subscription product can often be built once and sold repeatedly, with comparatively little incremental cost for each additional customer. That model can support recurring revenue and high gross margins. But the outcome depends on the product, delivery model, customer mix and company execution; it is not a guarantee for every software startup.
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AI products can also reuse software and serve many customers. The difference is that operating the product may require paid model calls or GPU capacity every time a customer uses it. A business may also need to prepare or maintain data, evaluate model outputs, tune workflows, integrate with customer systems, or pay people to review and correct results. Some AI products have little of this overhead; others depend on it heavily.
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For investors, the useful question is therefore not “Is AI less profitable than SaaS?” It is: What does it cost this company to deliver a reliable, valuable result to each customer, and does that cost improve as the business grows? That question links product design to unit economics, operational scaling, capital requirements and competitive durability.
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How cost to serve changes gross-margin analysis
Separate reusable product costs from usage-linked costs
In a conventional software subscription, much of the product-development expense is incurred before a customer uses the product. Serving another customer can add support, hosting and implementation work, but the core software may be reused with little additional labor. An AI application may reuse its interface and workflow while incurring a fresh inference or compute expense for each task. Higher usage can therefore increase revenue and cost together.
That does not mean every AI interaction is expensive or every additional user lowers margin. Costs depend on the workload: the model selected, the amount of input and output, the frequency of use, the need for retrieval or other processing, and the quality threshold the product must meet. A product can improve economics through more efficient workflows, suitable smaller models, caching or other technical choices, provided it still achieves the customer’s required accuracy and reliability. Investors should ask for observed costs under representative customer workloads rather than rely on a general claim about model prices.
Other recurring expenses can sit beside inference. Data may need labeling, cleaning, updating or customer-specific preparation. Teams may monitor output quality, investigate failures and maintain evaluations. Human reviewers may check high-risk or low-confidence outputs, or repair results before they reach the customer. If these activities grow roughly in proportion to usage, they can make an apparently automated product labor-intensive in practice.
Read margin figures as hypotheses, not sector benchmarks
An a16z article published around 2020 offered an anecdotal estimate of 50–60% gross margins for AI companies, compared with 60–80% or more for comparable SaaS businesses. Its note says the AI estimate came from founder interviews. This is an investor-publisher observation, not a current representative dataset, a universal sector average or a prediction for a particular company. Use it as a prompt to investigate cost structure, not as a hurdle that proves a company is good or bad.
Before comparing margins, establish what each company includes in cost of revenue. One company may count model/API fees, GPU and cloud charges, support, data operations and customer-specific tuning; another may report some of those costs elsewhere. A margin comparison is only meaningful if the accounting treatment is sufficiently consistent. Ask for the bridge from revenue to gross profit and identify which operating costs remain outside that calculation.
Gross margin is also not the whole unit-economic picture. Compare it with contribution margin and gross profit per customer, task or completed outcome. A high-revenue contract can be unattractive if usage, implementation and review costs consume most of the proceeds. Conversely, an early product with modest margin may be building repeatable delivery that improves as usage grows. What matters is whether the company can demonstrate a credible path from current workload economics to sustainable gross profit.
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Test what happens as customer use grows
Ask management to show costs and quality at different levels of usage. Does cost per task fall with volume, stay roughly flat, or rise because customers use the product for longer or more complex jobs? Does accuracy improve, deteriorate or remain stable as volume and task variety increase? If a customer adopts the system across more of its workflow, does the company earn more while incurring proportionate inference and review costs—or does each additional use require substantial human intervention?
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- Request cost per task or outcome for representative customer workloads, with the task definition and usage level made clear.
- Break out model/API, cloud or GPU, data operations, support and human-review expenses.
- Compare margin and gross profit across customer cohorts, use cases and contract sizes rather than relying on a single company-wide average.
- Find out whether custom tuning and customer-specific data work are included in implementation fees, recurring revenue or delivery costs.
- Check how costs and output quality change when volume, complexity or the required reliability level changes.
When AI delivery scales—and when it turns into a service
Look for repeatability across customers
Software can scale efficiently when a stable product handles many customers without requiring a new engineering project for each one. AI can follow the same pattern: a reliable product may automate a complete workflow, use a shared model or process, and deliver the same core capability across a broad customer base. In that case, the fact that inference costs recur does not by itself prevent attractive scaling.
The risk is that a promising demonstration may not represent routine production use. Real deployments can expose unusual inputs, edge cases and customer-specific rules that require evaluation, quality control, integration and manual correction. If the product needs extensive data preparation or bespoke engineering for every account, delivery may behave more like a consulting or managed service than reusable software. That can still be a viable business, but its labor needs, margins and growth capacity should be assessed accordingly.
Measure operational burden, not just model capability
Ask what proportion of outputs require human review, correction or escalation, and how that proportion varies by task and customer. Clarify whether the company measures success as a model response, an accepted output or a completed customer workflow. A system that produces plausible responses but leaves customers to check and finish the work may not deliver the labor savings implied by its product pitch.
Onboarding and implementation are another scaling test. Determine how long each deployment takes, which internal roles are involved, how much work the customer must do, and whether the work is repeatable. Track these measures by cohort: are newer customers going live faster and needing less support, or does each new implementation reveal another special case? A falling implementation burden is evidence that the product is becoming more repeatable; a rising burden deserves an explanation.
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- Implementation: How much work is required before a customer reaches production and realizes value?
- Customization: Can one model and workflow serve many customers, or does each contract create a bespoke engineering branch?
- Reliability: How are failures detected, measured and handled, and who bears the cost of recovery?
- Delivery capacity: Can customer growth outpace hiring in implementation, support and data operations?
What can make an AI startup defensible?
Model access is not the same as a moat
Access to a capable model can help a startup ship a useful product, but access alone may be difficult to defend if competitors can use broadly available models or providers introduce similar capabilities. The a16z analysis cautions that model access may not create a durable moat as models become commoditized. That is a strategic hypothesis, not a certainty about how any particular market will develop.
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Investors should separate the model layer from the value the company adds around it. A company may build a differentiated workflow, integrate with systems customers already use, develop deep domain expertise, establish distribution, or earn trust in a mission-critical setting. Those advantages can persist even if the underlying model changes. The reverse is also possible: a technically impressive product may be vulnerable if customers can switch to a provider or incumbent offering similar functionality with little friction.
Investigate context, rights and customer dependence
Proprietary context can strengthen a product when it helps the system perform a valuable task better over time. But “the company has customer data” is not enough. Establish what data the company can lawfully and contractually retain, use and learn from; whether the customer can export or delete it; and whether product use actually improves quality in a way competitors cannot quickly reproduce. Data access without usable rights, meaningful feedback or a measurable product benefit is not automatically a defensible asset.
PwC’s M&A commentary points to domain depth, ecosystem entrenchment, proprietary context and integration into mission-critical workflows as potential sources of resilience. These are useful diligence lenses rather than guarantees. Test switching costs in practice: how difficult would it be for a customer to replace the product, and what would have to be rebuilt, retrained or reapproved? Also ask whether distribution, customer relationships or trust are stronger defenses than model differentiation.
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- What does the company own or have durable rights to use: software, workflow knowledge, proprietary context, distribution or customer relationships?
- Does usage create a feedback loop that measurably improves the product, and can the company retain the relevant learning?
- How embedded is the product in the customer’s workflow, and what evidence supports switching costs?
- Could a model provider, incumbent software vendor or new entrant add the same feature? What would still distinguish this company?
Capital intensity depends on which AI business is being built
Do not treat all AI funding as evidence about application startups
AI investment includes businesses with very different capital needs. An application company using third-party models has a different cost and funding profile from a company training and serving large models or building infrastructure. The latter may require substantial compute capacity and investment before it can generate revenue at scale. The former may avoid owning that infrastructure but remain exposed to provider pricing, availability and product changes. “AI company” is therefore too broad a category for judging runway or funding needs.
The OECD’s 2026 analysis of 2025 venture-capital data reports that AI firms received USD 258.7 billion, or 61% of global VC investment of USD 427.1 billion. About 73% of AI investment value came through mega-deals over USD 100 million. These figures show a large and concentrated flow of capital; they do not show that a typical AI startup can raise at similar terms, that all AI subcategories are equally funded, or that funding momentum predicts returns.
The same OECD report says AI IT infrastructure and hosting companies attracted USD 109.3 billion in VC in 2025. Cumulative investment in that category from 2012 through 2025 was USD 256.1 billion. These are capital-flow figures, not measurements of startup-level unit economics. The OECD notes that its data are drawn from Preqin via OECD.AI, last updated 2026-01-01, and that classification methods may evolve.
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Use UK sector figures and survey responses within their scope
A 2025 UK Department for Science, Innovation and Technology (DSIT) sector study estimated 2024 UK AI-sector revenue at £23.9 billion and employment at 86,139; the estimates were produced by Perspective Economics. The study drew on secondary data, responses from 298 AI companies and 52 in-depth interviews. These figures describe the UK sector under that study’s methodology, not a global startup cohort or a forecast for an individual company.
In the study’s survey, 53% of respondents said access to equity investment had significantly affected their ability to meet business goals in the previous 12 months. That is a reported survey response, not a measurement of the share of all UK AI firms facing a funding constraint. Separately, 66% of respondents (n=196) expected their need for computing power to increase, while 71% expected increased needs for skills and for software/development tools. The study also records concerns among some firms about dependence on foreign cloud and LLM infrastructure. Treat these as UK survey evidence about reported expectations and concerns, not as universal forecasts.
Stress-test infrastructure and supplier dependencies
A company that relies on external models or cloud services can get to market without financing a large training program, but it still needs a plan for supplier dependency. Ask what happens to margins if model or cloud prices change, a provider changes terms or availability, or a product is withdrawn. Find out whether the team can switch providers, route work to a suitable smaller or open model, or redesign the workload without materially worsening quality and reliability.
For companies building infrastructure or training models, assess whether spending creates reusable assets and a credible route to revenue—or mainly defers the point at which unit economics are tested. In either case, estimate the capital needed to reach the next meaningful milestone, such as a repeatable production deployment or evidence of durable retention. Funding totals across the AI market are not substitutes for a company-specific runway plan.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare pricing, retention and valuation
Match the price metric to the value delivered
Seat-based subscriptions are easy to compare when customer value and product use track the number of licensed users. AI may change that relationship. A customer may use automation to reduce the number of people doing a task, so seats can contract even when the product is performing well. PwC’s advisory commentary suggests looking at cohort and gross revenue retention as well as net revenue retention, because seat contraction can obscure parts of the picture. That interpretation must be tested against the company’s own contracts, renewals, usage and realized customer value.
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Make retention and customer economics cohort-specific
Review annual contract value alongside actual usage, renewal behavior and customer concentration. Separate gross revenue retention, which captures retained revenue before expansion, from net revenue retention, which includes expansion and contraction. Analyze both by cohort and customer segment to distinguish durable product value from growth driven by a few large accounts or unusually heavy usage. For contracts priced on outcomes, check how often the agreed outcome is reached and whether the company can deliver it profitably.
Implementation expense belongs in this analysis, even if it is recorded separately from recurring cost of revenue. A contract can show attractive recurring revenue while requiring so much upfront work that acquisition and delivery economics are weak. Likewise, reported retention may look strong if a small number of customers expand while others reduce use. The goal is to connect what customers pay to the cost and work required to keep delivering value.
Avoid applying a SaaS multiple mechanically
A revenue multiple cannot answer whether a company’s growth is high quality or durable. Before using conventional SaaS comparisons, understand cost to serve, gross profit, customer retention, customer concentration, pricing power and the company’s need for future capital. A fast-growing business with heavy usage-linked expense and repeated custom work may have a different risk profile from a software company with more reusable delivery, even if both report recurring revenue. The comparison should follow demonstrated economics, not the label attached to the product.
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Use these questions to turn a category-level comparison into a company-level underwriting view. Answers should be supported by customer contracts, usage records, cohort data and operating-cost detail—not only by management’s long-term projections.
- Define the product and workload. What task or workflow does the customer buy, what counts as a successful result, and how frequently is it used?
- Rebuild cost of revenue. Identify treatment of model/API fees, GPU and cloud, support, data operations, human review and customer-specific tuning.
- Measure unit economics. Compare gross margin, contribution margin and gross profit per customer, task or outcome across representative workloads.
- Check whether economics improve with scale. Compare cost, quality, review rates and implementation burden as use and customer count grow.
- Test repeatability. Determine how much onboarding, integration and custom engineering each deployment requires, and whether those demands are falling by cohort.
- Verify customer value and retention. Review usage, renewals, gross and net revenue retention, concentration and customer ROI by cohort.
- Assess pricing risk. For usage or outcome-based contracts, check whether the billable measure is linked to customer value and whether delivery remains profitable.
- Examine defensibility. Identify workflow integration, domain expertise, proprietary context with valid usage rights, distribution, trust and switching costs.
- Stress supplier exposure. Model the effect of changes in model or cloud costs, availability and terms; assess realistic alternatives.
- Calculate milestone capital. Estimate runway and funding required to reach the next evidence point, and distinguish productive investment from spending that postpones an economics test.
Conclusion: underwrite the economics beneath the category
Traditional software often benefits from low marginal delivery costs once a product is built. AI software may retain that advantage, but its economics can also include usage-linked compute, ongoing data operations and human quality control. Those costs are not destiny: architecture, workload design, workflow automation and repeatable deployments can make a substantial difference.
For investors, the best comparison is company-specific. Follow the path from customer value to revenue, from revenue to fully understood delivery cost, and from current performance to the capital required for the next milestone. A strong AI investment case rests on evidence that the product can deliver reliably, retain customers and build durable advantage at sustainable economics—not on funding momentum or an AI label alone.
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