Strategic buyers and financial buyers are the two main archetypes of acquirers in M&A, valuing targets differently and structuring deals differently. Strategic buyers are operating companies acquiring for synergies, capabilities, market access, talent, or product fit (Salesforce buying Slack, Adobe attempting to buy Figma, Microsoft buying LinkedIn). Financial buyers are private equity firms, growth equity firms, or other capital pools acquiring primarily for financial returns (Vista acquiring Marketo, Silver Lake buying various companies). Each archetype also treats management teams differently. Understanding which type of buyer is pursuing your company shapes how you negotiate.
The core difference:
Strate...
The product lifecycle is the four-stage model of commercial life through introduction, growth, maturity, and decline, used to inform investment, pricing, and sunset decisions. Introduction covers launch and early adoption; growth covers rapid adoption, scale, and competitive entry; maturity covers slowing growth and pricing pressure; decline covers replacement by alternatives and eventual sunset. The framework was popularized by Theodore Levitt in his 1965 Harvard Business Review article "Exploit the Product Life Cycle" and has been adapted from physical-product marketing into software product management.
The four classical stages with their typical characteristics: introduction (low sales volume, high per-unit cost, focus...
Gross margin is revenue minus cost of goods sold (COGS) expressed as a percentage of revenue. It represents the portion of revenue available to cover operating expenses (sales, marketing, engineering, G&A) and ultimately produce profit. Gross margin varies dramatically by business model: SaaS typically 70-85%, physical goods 20-50%, marketplaces variable by take rate, services 40-70%. It's one of the most-important indicators of business model quality because operating costs are largely fixed at scale, and gross margin determines the ceiling on profitability. It distinguishes economically scalable business models from ones that struggle to ever become profitable.
The calculation:
Basic formula:
The CTO (Chief Technology Officer) is the highest-ranking technical executive of a company, responsible for technology strategy, architecture decisions, engineering leadership, and build-vs-buy evaluation. The CTO also serves as the technical face of the company externally to customers, partners, technical recruiting, and press. The role evolves dramatically from "hands-on builder writing most of the code" at early stage to "executive technology leader overseeing 50+ engineers and tech strategy" at scale-up and beyond. It is one of the most context-dependent C-suite roles, varying significantly by company stage, technology category, and CEO/co-founder dynamics.
The role evolution across stages:
Early-stage CTO (pre-Series A, 1-10 engine...
Token economics is the discipline of understanding and modeling the per-token costs and revenue of AI applications. Tokens (sub-word units of text) are the unit of pricing for most LLM APIs and the basis on which AI application unit economics must be modeled. The model includes input tokens (prompt + context + RAG content), output tokens (model response), cache savings, and the per-query economics that determine whether AI applications are profitable. It's the financial layer beneath every AI application.
What a token is:
Token: sub-word unit of text used by LLMs. Roughly 0.75 English words per token, or 4 characters.
Tokenization examples:
An earnout is a contingent acquisition payment tied to the acquired company hitting post-close milestones over a defined performance period, typically 1 to 3 years. Milestones cover revenue, EBITDA, product launches, customer-retention thresholds, or other operating metrics, and the structure is used to bridge valuation gaps between buyer and seller when the buyer doesn't want to pay up front for value that depends on future performance. It is one of the most-negotiated and least-loved acquisition mechanics, because it transfers performance risk from buyer to seller and gives the seller limited control over the metrics they're now paid to hit.
The typical structure: an earnout represents 10 to 40 percent of total deal value (sometim...
Retrieval-Augmented Generation (RAG) is the AI pattern of pulling relevant context from a knowledge base and including it in the LLM prompt at inference time. Retrieval typically uses vector search. RAG allows the model to answer questions or perform tasks using information it wasn't trained on, or that may be more recent than its training data. RAG has been the dominant pattern for knowledge-grounded AI applications since 2023. It's the bridge between general-purpose LLMs and specific organizational knowledge.
How RAG works (the pipeline):
The discipline of turning awareness into customers and customers into expansion. This cluster covers the full marketing funnel from awareness to advocacy, every major acquisition channel (paid, organic, content, referral, influencer, affiliate), the metrics that govern each (conversion, CPA, CPC, ROAS), retention and customer success (NRR, churn, NPS, onboarding), brand (identity, voice, positioning, awareness), and the lifecycle marketing patterns that compound. 62 entries.
This is the cluster where the most cross-discipline vocabulary lives. Founders without marketing backgrounds need every term here.
The Jobs Framework (Jobs-to-be-Done or JTBD) is the strategic approach that focuses on the "jobs" customers hire products to do rather than demographics or features. Popularized by Clayton Christensen and Tony Ulwick, the central question is "what job is this customer trying to get done when they hire this product?" The framework provides a customer-outcome lens distinct from feature-focused approaches (what does the product do?) and segment-focused approaches (who is the customer?), arguing that jobs are more stable predictors of demand than demographics or features. It is one of the more-useful strategic frameworks for product and business strategy.
The core concept:
Customers don't buy products; they hire products for jobs...
A liquidation waterfall is the calculation that determines how exit proceeds are distributed across preference stack, share classes, and option pools at exit. Exit proceeds include acquisition cash, public offering proceeds, and dissolution distributions. The waterfall is modeled as a series of "buckets" that fill in priority order until the proceeds are exhausted. It is the math that determines what each shareholder actually receives, and the analysis founders most consistently postpone until it's too late to change.
The waterfall fills in roughly this order: secured debt first (rare for venture-backed startups, but present if there's outstanding venture debt with collateral), then unsecured debt and trade obligations...