Software, Platforms & AI Industry

Decompose recurring-revenue growth into new-customer acquisition, expansion, contraction, churn, and pricing effects.

Calculate and interpret gross retention, net revenue retention, customer acquisition payback, and lifetime-value ratios.

Explain how on-premise licensing, subscription SaaS, and consumption-based delivery produce different margin and cash-flow profiles.

Assess the strength of switching costs created by data, workflows, integrations, skills, and contractual commitments.

Compare seat-based, usage-based, tiered, and outcome-based pricing for a software business.

Identify when bundling, cross-selling, and ecosystem partnerships strengthen or weaken a software company's strategic position.

Analyze how Microsoft combines installed base, bundling, cloud infrastructure, and distribution to compete across software categories.

Compare Salesforce's customer-platform expansion with ServiceNow's workflow-platform expansion.

Assess how SAP and Oracle use installed bases, migration programs, and cloud portfolios to defend enterprise accounts.

Compare Snowflake, Databricks, and hyperscaler data platforms by workload ownership, ecosystem role, and source of differentiation.

Map competitive positions across enterprise applications, data platforms, infrastructure software, and developer tooling.

Evaluate an unfamiliar software company using its category position, growth engine, retention profile, and strategic control points.

Map the distinct participant groups, exchanges, and monetization flows in a multi-sided platform.

Distinguish same-side and cross-side network effects and assess their strength using observable evidence.

Select which participant group a platform should subsidize under alternative growth and monetization conditions.

Diagnose platform risks arising from disintermediation, multi-homing, congestion, and adverse selection.

Evaluate how access rules, ranking systems, fees, and enforcement choices affect platform health.

Construct a payoff matrix to analyze a platform's likely response to entry, bundling, or fee changes.

Assess how antitrust, data, content, and labor regulation can alter a platform's economics and strategic options.

Compare Alphabet's and Meta's advertising flywheels through data access, inventory, measurement, and advertiser demand.

Analyze Amazon's interactions among marketplace liquidity, fulfillment, advertising, Prime, and cloud economics.

Compare Apple and Google as mobile ecosystem governors using distribution control, developer economics, and default-setting power.

Assess marketplace defensibility using the contrasting operating models of businesses such as Uber and Airbnb.

Map the payments value chain and compare the strategic roles of networks, issuers, acquirers, processors, wallets, and payment service providers.

Compare Stripe, Adyen, PayPal, and incumbent payment providers by customer segment, control point, and expansion strategy.

Trace a user request through the client, network, application, compute, data, and observability layers of a representative cloud service.

Distinguish IaaS, PaaS, serverless, and SaaS by customer responsibility, provider control, and economic model.

Explain the strategic purpose of APIs in product integration, ecosystem expansion, and distribution.

Compare monolithic and microservices architectures in terms of delivery speed, reliability, and operating complexity.

Explain how containers and orchestration support deployment and infrastructure utilization.

Map operational responsibilities for availability, cybersecurity, identity, data protection, and disaster recovery in a cloud service.

Use FinOps metrics to identify the principal drivers of cloud cost and gross-margin pressure.

Map the AI infrastructure stack from semiconductors and networking through data centers, cloud services, and model providers.

Distinguish the compute, memory, networking, and latency requirements of model training and inference.

Calculate how accelerator utilization, power consumption, depreciation, and model demand affect AI compute unit economics.

Compare NVIDIA GPUs, competing accelerators, and custom silicon by ecosystem strength, workload fit, and buyer incentives.

Explain how power delivery, cooling, networking, and rack density constrain AI data-center design.

Identify capacity and supply-chain bottlenecks across chips, advanced packaging, servers, power, construction, and grid connections.

Evaluate build, lease, cloud, and partnership options for securing AI capacity.

Assess an AI infrastructure investment using demand scenarios, utilization risk, time-to-capacity, and return on invested capital.

Distinguish an AI-native company from an AI-enabled incumbent using product architecture, operating model, and value capture.

Map the foundation-model value chain across data, compute, model development, hosting, tooling, applications, and distribution.

Calculate the principal revenue and inference-cost drivers of an AI product's unit economics.

Compare the strategic positions of major model providers using performance, cost, distribution, enterprise readiness, and ecosystem reach.

Distinguish prompting, retrieval-augmented generation, fine-tuning, tool use, and agentic workflows by purpose and operating implications.

Assess potential AI moats arising from proprietary data, workflow integration, feedback loops, distribution, cost advantage, and trust.

Evaluate whether an AI application has a defensible business model rather than a replaceable feature.

Analyze how incumbent software firms can respond to AI-native entrants through bundling, acquisition, pricing, or product redesign.

Formulate a product-portfolio thesis that links customer problems, technology shifts, strategic assets, and investment choices.

Assess how telemetry, experimentation, and customer research inform product prioritization in a scaled software business.

Evaluate a transition from seat-based pricing to consumption- or outcome-based pricing.

Select among direct sales, product-led adoption, marketplaces, partners, and embedded distribution for a defined customer segment.

Diagnose conflicts among product architecture, packaging, sales incentives, customer success, and partner economics.

Design decision criteria for building, buying, or partnering to add an AI or platform capability.

Decompose platform operations into customer support, trust and safety, content or data operations, payments operations, and infrastructure support.

Design a human-and-AI workflow that assigns tasks according to risk, judgment, repeatability, and automation potential.

Define quality, productivity, latency, risk, and cost metrics for a scaled platform-service operation.

Evaluate captive, outsourced, managed-service, and hybrid delivery models for a platform capability.

Assess how localization, regulation, fraud patterns, and market maturity change global service delivery.

Build an automation business case that accounts for implementation cost, exception handling, model risk, and workforce transition.

Assess a software or platform company's maturity across Product, Growth, Distribution, People, and Enabling Functions.

Identify cross-value-chain reinvention opportunities enabled by cloud, data, AI, and operating-model changes.

Design a target operating model that assigns decision rights across product, engineering, sales, operations, finance, and partners.

Sequence a reinvention roadmap according to technical dependencies, organizational readiness, value, and risk.

Construct a transformation business case linking operational drivers to revenue, margin, cash flow, and resilience.

Define leading and lagging indicators for tracking whether a reinvention program is delivering value.

Create a source hierarchy for monitoring companies, technologies, capital flows, regulation, and customer demand.

Build a reusable competitor profile covering business model, economics, customers, capabilities, partnerships, and strategic moves.

Interpret an earnings release as evidence of changes in demand, competitive position, investment priorities, and execution risk.

Assess the strategic implications of a product launch, acquisition, partnership, pricing change, or regulatory action.

Benchmark a peer group using comparable financial and operating metrics while accounting for business-model differences.

Construct alternative industry scenarios from uncertain shifts in technology, regulation, customer behavior, and capital availability.

Translate an industry signal into testable client hypotheses and relevant Product and GTM, Platform Services, AI Compute, or Reinvention opportunities.

Produce an executive industry brief that separates facts, interpretation, implications, uncertainties, and recommended actions.