The reliable way to compare AI companies is to compare the specific business layer, product, customer and revenue stream—not to assign every company one broad “AI” label. Separate infrastructure (chips, servers, networking and cloud), applications (software and services used in workflows), and robotics or physical AI (autonomy combined with machines, integration and field operations). Then compare evidence that fits each layer’s economics and deployment conditions.
A cloud provider can sell infrastructure, platform tools and model access at the same time. A chip company can also provide software. A robotics business may combine autonomy software, vehicle or robot platforms, engineering and service contracts. The framework below keeps those activities distinct while showing how they connect.
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Why “AI company” is too broad a category
AI businesses sit at different points in the value chain. Infrastructure supplies the computing and systems needed to train and run models. Applications turn those capabilities into tools for customers or employees. Robotics and physical AI add a machine, a real environment and operational responsibility. These layers are connected, but they do not have the same buyers, costs, risks or useful performance measures.
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Infrastructure includes accelerators and other chips, CPUs, servers, networking, data-center systems, cloud computing and platform services. AMD’s company reporting describes data-center accelerators, CPUs, networking and software. Alibaba describes infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS) and model-as-a-service (MaaS) within its cloud business. A comparison should therefore specify whether it concerns a component, a complete system or a cloud service.
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Applications
Applications are software and services that place AI inside a business or consumer workflow. They may expose a model through a product, automate a process, assist an employee or deliver a specialized service. NVIDIA’s FY2026 shareholder letter describes this layer as the point where infrastructure becomes economic and social value: “Applications are where AI infrastructure becomes productivity, growth, and national capability.” That is a description of NVIDIA’s view, not an independent ranking of application companies.
Robotics and physical AI
Robotics companies combine autonomy software with robots, vehicles or other machines. They may also provide integration, engineering, manufacturing and ongoing maintenance. Palladyne’s filing describes autonomy products for industrial robots and unmanned platforms. A software demonstration is not equivalent to a robot performing a task repeatedly, safely and economically at a customer site.
Companies can span several layers
These categories describe the offering or segment being compared, not an exclusive identity for the whole corporation. NVIDIA’s letter says applications draw on every layer beneath them. Alibaba reports infrastructure, platform and model services in one cloud business. Palladyne combines software, hardware and engineering. Comparing “Company A” with “Company B” without identifying the segment can produce a meaningless result.
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Start with a business map before looking at benchmarks or market share. Record the smallest unit for which the company reports a product, customer or financial result.
- Name the paying customer. Is the buyer a cloud operator, model developer, enterprise department, government agency, factory, warehouse, fleet operator or individual? A user of a free tool is not necessarily a paying customer.
- Describe the purchased offering. Write down the chip, server, cloud capacity, API, software workflow, robot platform or service actually delivered.
- Classify the revenue mechanism. Separate hardware sales, cloud consumption, model or API usage, software subscriptions, integration work, deployment fees and robotics service revenue when the company discloses them.
- Mark the reporting scope. Label each number as company-wide, segment-level, product-level or management estimate. Do not substitute total corporate revenue for AI revenue.
- Record the period and geography. Keep the fiscal period, currency, region and denominator beside every figure. A quarterly result, a fiscal-year result and an annualized run rate are different observations.
| Layer | Offering to identify | Possible payer | Revenue evidence to look for |
|---|---|---|---|
| Infrastructure | Accelerator, CPU, networking, server, data-center system, IaaS, PaaS or MaaS | Cloud provider, enterprise, model developer or public-sector buyer | Product sales, cloud consumption, capacity commitments, segment revenue and utilization |
| Applications | Workflow software, model-enabled service, API or managed application | Business team, enterprise, developer, consumer or government customer | Paid seats, usage, subscriptions, recurring revenue, retention and deployment evidence |
| Robotics and physical AI | Autonomy stack, robot or vehicle, integration, engineering or service package | Factory, warehouse, fleet, infrastructure operator or public agency | Systems deployed, paid operations, service contracts, maintenance and repeat orders |
Alibaba’s reporting shows why this separation matters: its cloud business spans infrastructure, platform and model services, while its release separately identifies AI-related products within cloud external revenue. A cloud segment’s total revenue cannot automatically be called AI revenue.
How to compare infrastructure businesses
Infrastructure comparisons should answer whether a system can deliver the required workload at acceptable cost, availability and operational risk. A peak benchmark number alone is insufficient.
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Performance and capacity
Match the measurement to the workload: model training, inference, high-throughput batch processing, interactive latency or general-purpose enterprise computing. Record the precision, model size, batch size, memory, interconnect and software stack used in any stated result. Capacity also includes how many accelerators or instances can be obtained and operated together, not just the speed of one device.
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Power delivery, cooling, rack density and network fabric can determine whether theoretical performance is usable. Ask whether the product requires specialized networking, proprietary systems or unusually high power. For cloud services, compare the availability of the relevant instance in the reader’s region and the limits on scaling or reserved capacity.
Software ecosystem and portability
Examine compiler support, libraries, orchestration, monitoring, model compatibility and the effort required to move an existing workload. A less expensive chip may be uneconomic if engineers must rewrite software or maintain a smaller toolchain. Conversely, a mature ecosystem can reduce deployment time even when hardware prices are similar.
Cost, utilization and customer concentration
Use the price for the actual deployment, including storage, networking, support, power and idle capacity. Cloud consumption prices are not equivalent to a hardware purchase price. Check whether revenue depends on a small number of large customers or on broad utilization. A supplier can report strong demand while a buyer still faces shortages, long lead times or low utilization.
Infrastructure comparison checklist
- What workload and performance metric are being measured?
- What capacity, power, cooling and networking are required?
- Which software frameworks, libraries and models run without modification?
- What is the delivered cost at the expected utilization, not just the list price?
- How available is the product in the required geography and time frame?
- Is the evidence a product specification, a customer deployment, a segment result or a management claim?
How to compare AI application businesses
Application companies should be judged by the workflow they improve and the cost and reliability of delivering that improvement. Model quality matters, but a technically impressive model can still fail if it does not fit the customer’s process.
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Accuracy and task performance
Define the task and the acceptable error. For a customer-support tool, this may involve correct resolution and escalation; for document processing, extraction accuracy and review time; for a developer tool, successful code changes and defect rates. Do not compare an accuracy figure from one task with a score from another.
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Workflow fit and integration
Measure the work required to connect the product to identity systems, data stores, business applications and approval steps. Deployment may be cloud-based, on-premises or hybrid. Data access, permissions, audit trails and human review can matter more than a small model-score difference.
Adoption, repeat usage and cost to serve
Look for paid customers, active usage, renewal or retention where disclosed, and evidence that users return after a trial. Separate one-time implementation revenue from recurring subscriptions or consumption. Estimate the cost of inference, storage, support, human review and customer-specific integration; an application with high usage can still have weak economics if each request is expensive to serve.
Security, reliability and scalability
Ask how the application handles outages, incorrect outputs, sensitive information and changing workloads. Reliability should be observed in the intended deployment, not inferred from a demonstration. Security claims should identify the deployment model, controls and customer obligations rather than relying on a generic “enterprise-grade” label.
Issuer-stated competitive factors
Veritone’s 2025 Form 10-K lists the following factors in its own competition discussion: “Applications to enable our platform to be effectively leveraged for a wide variety of use cases”; “Breadth, depth and performance of cognitive processing and other AI capabilities, particularly accuracy and speed”; “Availability of cloud-based and on-premises deployment models and functionality”; “Ease of deployment and integration”; “Platform scalability, reliability and security”; and “Cost of deploying and using our products.” These are the issuer’s stated criteria, not an independent universal scoring standard, but they form a useful application due-diligence checklist.
How to compare robotics and physical-AI businesses
Robotics comparisons must include the physical environment. A system that works in a controlled demonstration may struggle with lighting, clutter, changing objects, network interruptions, weather, vibration or human interaction.
Task reliability in the real world
Define the task, operating conditions and intervention policy. Useful evidence includes successful cycles, failure modes, recovery time, the percentage of work requiring a human and performance across different sites or object types. A single demonstration does not establish repeatability.
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Hardware compatibility and integration
Identify which robots, vehicles, sensors, controllers and safety systems the autonomy software supports. Integration with a customer’s existing equipment can determine the project timeline and cost. A platform limited to one hardware configuration has a different market opportunity from software that operates across several platforms.
Safety and human oversight
Record emergency-stop behavior, operating envelopes, access controls, monitoring and the human role in supervision. “Autonomous” may mean continuous operation with remote monitoring, or it may mean a human approves every action. Those models have different labor requirements and risk profiles.
Uptime, maintenance and service economics
Compare uptime under actual operating conditions, preventive maintenance, spare parts, software updates and field-service coverage. Include installation, site preparation, training and integration in deployment cost. A recurring service contract can produce a different revenue profile from a one-time robot sale.
Evidence from deployments
Palladyne’s filing describes intended uses and platform coverage across industrial robots and unmanned platforms. Treat those descriptions as company-reported capabilities. To establish operating performance, seek paid deployments, task outcomes, service requirements and repeat orders; do not infer results from product breadth alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Normalize financial evidence before comparing companies
Place every number beside its scope, date, currency and denominator. Reported historical results, annualized measures and forecasts must remain separate.
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|---|---|---|
| $216 billion revenue; $130 billion operating income; $194 billion data-center revenue, up 68% year over year | NVIDIA Corporation FY2026 company and data-center results | The first two are company-level annual figures; data-center revenue is a segment measure and not total AI-market revenue. |
| RMB 41,626 million Cloud Intelligence Group revenue; 40% external-revenue growth | Alibaba Group results for the referenced March quarter in its 2026 release | These are period-specific company results; do not treat the quarter as a full-year result. |
| AI-related products were 30% of Cloud external revenue | Alibaba’s stated mix for the referenced period | The denominator is Cloud external revenue, not Alibaba’s total revenue. |
| More than RMB 35.8 billion annualized AI-related product revenue | Alibaba’s annualized measure | Annualized revenue is not audited full-year revenue; keep that qualification with the number. |
| Fourth worldwide and first in Asia Pacific for 2024 IaaS revenue | Gartner’s April 2025 ranking, as cited by Alibaba in its FY2025 annual report | The ranking is for 2024 IaaS revenue and the stated geography, not all cloud services or the AI market. |
Percentages are comparable only when their numerators and denominators are defined on a compatible basis. A company’s total revenue may include businesses unrelated to AI. Conversely, a disclosed AI-related product figure may cover only one cloud segment. Keep segment and product labels in the table or spreadsheet where you perform the comparison.
Test technical claims in the intended deployment
Every benchmark or product claim needs context. Ask who measured it, on which hardware or model, under what workload, and with what software and service assumptions. Compare quality, latency, throughput, reliability, security, integration, interoperability, deployment model and total cost together.
| Question | Infrastructure | Applications | Robotics |
|---|---|---|---|
| What is the unit of performance? | Training time, inference throughput, latency or usable capacity | Task accuracy, resolution rate, time saved or cost per completed task | Successful cycles, intervention rate, uptime or task completion |
| What constrains scale? | Power, cooling, networking, supply and software compatibility | Data access, integration, inference cost, support and customer workflow | Site conditions, hardware fleet, safety envelope, maintenance and connectivity |
| What evidence is strongest? | Independent or reproducible testing plus customer deployment | Paid usage, retention and measured workflow outcomes | Repeated paid operations, service records and outcomes across sites |
Company filings and releases are useful primary evidence for what an issuer reports, but they do not prove that a product is best. Microsoft’s descriptions of cloud competition, for example, place providers alongside open-source offerings; that framing identifies the competitive set without establishing a winner. Treat company-described capabilities as issuer claims unless independent testing is available.
Separate present evidence from plans and forecasts
Use a separate column for historical results, current operating evidence, management expectations and intended strategy. Palladyne’s filing warns that statements about plans and strategy involve uncertainty. Alibaba’s expectations for future AI revenue are management forecasts, not realized results.
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- Historical result: a reported revenue, margin, deployment or customer figure for a completed period.
- Current operating evidence: an existing product, paid usage, installed system or disclosed service result.
- Annualized measure: a run rate calculated from a shorter period; it is not the same as audited annual revenue.
- Forecast or plan: management’s expectation, target or intended use that may not occur.
- Marketing capability: a company-described feature or ambition that still requires deployment evidence.
Do not convert a forecast into a fact by placing it in the same column as reported revenue. Add the date and the speaker or issuer whenever management commentary is material.
A repeatable comparison worksheet
Use one row per company segment or offering rather than one row per corporate name. The following fields keep comparisons auditable:
Quick Recap
| Field | What to record |
|---|---|
| Layer | Infrastructure, application, robotics or a clearly defined combination |
| Payer and use case | Who pays, what job is being performed and where it runs |
| Offering | Product, service, hardware, software or integrated system |
| Revenue mechanism | Sale, subscription, consumption, API usage, integration or service |
| Reported AI exposure | Company, segment or product figure with period, currency and denominator |
| Performance evidence | Metric, test conditions, deployment sample and whether independently verified |
| Deployment burden | Integration, hardware, data, safety, training, maintenance and human oversight |
| Economics | Price, cost to serve, utilization, recurring component and capital requirements |
| Uncertainty | Missing disclosures, forecasts, concentration, availability and unresolved technical risks |
- Choose the precise offering or segment rather than the corporate label.
- Write the buyer, task and revenue mechanism in one sentence.
- Collect the period-specific financial and operating evidence.
- Match the performance metric to the intended deployment.
- List integration, power, safety, maintenance and human-supervision requirements.
- Separate reported results from annualized figures and forecasts.
- Mark unavailable values as “not stated” with the source and date instead of estimating them.
- Make a conclusion only after the evidence is normalized; do not create a single score when the layers use incompatible measures.
The reader’s final checklist
- Layer being compared: infrastructure, applications or robotics.
- Actual payer and purchased offering.
- Revenue mechanism and reporting scope.
- AI-specific exposure, with period, geography, currency and denominator.
- Performance evidence under the intended workload or physical conditions.
- Deployment, integration, reliability, security, safety and maintenance burden.
- Total cost, utilization, recurring revenue and capital requirements.
- Clear separation between realized results, annualized rates and management forecasts.
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