B Corporation

A B Corporation is either a private certification from B Lab verifying social and environmental performance, or a benefit-corporation legal status that codifies stakeholder primacy. The two are frequently conflated but distinct: the B Lab certification is private and reputational, requiring an 80+ score on the B Impact Assessment plus public transparency and recertification every 3 years. Benefit corporation status (called PBC in Delaware) is a corporate-law election available in roughly 40 US states (including Delaware, California, Texas, Colorado) that modifies director fiduciary duties to require consideration of stakeholder interests alongside shareholders.

B Lab certification (the private certification): B Lab, a nonprofi...

Legal Structure

The legal architecture that holds a startup together. This cluster covers entity types and formation (LLC, C-corp, Delaware), governance (board, officers, fiduciary duty), IP protection (trademark, patent, copyright, work-for-hire), employment law (NDAs, non-competes, employment agreements), commercial contracts (MSA, indemnification, arbitration), and privacy/compliance (SOC 2, GDPR, DPAs). 46 entries.

Founders skip this stuff until they can't. The cost of getting it right early is low; the cost of getting it wrong is brutal at diligence or in court.

Entity formation and types

• [Incorporation], the act of forming an entity.
• [Articles of Incorporation], the founding charter.
• [Bylaws], the operating rules.
• [C Corporation], ve...

AI Agent

An AI agent is an LLM-powered system that plans, uses tools, and takes actions over multiple steps to complete tasks autonomously. Tools include APIs, code execution, web browsing, and file operations. Agents go beyond single-prompt question-and-answer to handle complex workflows requiring reasoning, tool use, and iterative correction. "Agentic AI" is the dominant 2025 frontier for AI applications and the next major capability layer beyond chat. Agents are what happens when LLMs stop just answering questions and start doing things.

What distinguishes agents from simpler LLM applications:

Multi-step reasoning: agents break complex tasks into steps and execute each.

Tool use: agents call APIs, run code, browse the web, query database...

Continuous Discovery

Continuous discovery is the practice of conducting weekly customer touchpoints by the product trio (PM, designer, engineer) to inform ongoing product decisions. It was popularized by Teresa Torres in Continuous Discovery Habits (2021) and is structured around mapping desired outcomes to opportunities to solutions to assumption tests, rather than running discovery in concentrated batches separate from delivery. It is the operational evolution of Marty Cagan's product-discovery framing, focused on making customer evidence a weekly rhythm rather than a project.

The core practices Torres specifies: weekly customer touchpoints (at minimum, a 30-minute conversation with a real customer each week, by the whole trio, not just t...

Product

The discipline of building things people want. This cluster covers product management as a function, strategy and discovery, the agile/scrum/kanban operating frameworks, prioritization methods (RICE, Kano), design and UX, testing (alpha, beta, usability), and launch mechanics. 40 entries.

Product is where insight meets execution. The frameworks here are the operating systems most modern product orgs run on.

Product management roles and function

• [Product Management], the discipline and the role.
• [Product Team], how product orgs are structured.
• [Chief Product Officer], the senior product role.

Strategy and discovery

• [Product Strategy], the answer to "what we're building and why."
• [Product Vision], the longer-term north star.
• [Produ...

Agile

Agile is an iterative software development philosophy formalized in the 2001 Agile Manifesto, emphasizing working software, customer collaboration, and responsiveness to change. It rejects rigid up-front planning, contract negotiation, and process compliance, implemented through frameworks like Scrum, Kanban, Extreme Programming (XP), and at large-organization scale, SAFe and LeSS. It is the dominant operating philosophy of modern software product teams and the most-misunderstood word in the discipline.

The Agile Manifesto, written in February 2001 by 17 software practitioners at a ski lodge in Snowbird, Utah, states four values: individuals and interactions over processes and tools; working software over comprehensive documentation; ...

Growth & Marketing

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 marketing funnel and growth model

• [Marketing Funnel], the awareness-to-purchase path.
• [Growth Marketing], broader t...

Token Economics

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:

• "The quick brown fox" → 5 tokens (one per word, roughly).
• "Tokenization" → 3 tokens ...

AI Strategy

The vocabulary of the AI era of startups. This cluster covers the foundational concepts of modern AI (foundation models, LLMs, generative AI), the architecture and operations that power AI applications (Transformer, training data, fine-tuning, prompt engineering, RAG, context window), the economics that determine viability (inference cost, GPU cost, token economics), the strategic moats AI companies build (data flywheel, AI moat, wrapper vs thick wrapper), the safety considerations (alignment, safety), and current-era terms (multimodal, agents, vibe coding). 22 entries.

This cluster is the freshest in the lexicon. If you're building anything AI-adjacent in 2025, every entry here is operational vocabulary.

Foundations

• [AI Startu...

Every Founder lies about why they are building a startup.

It's not because they are deceptive; I find Founders to be the most vulnerable and honest people I've ever met. It's because they aren't honest with themselves about why they are really building their startups.

When you ask a Founder why they are building, they are inevitably going to give you a stock answer like "I want to change the world!" or "I want to build a huge company!" and, of course, that sounds flowery and wonderful.

But that's not the real answer. The real answer is why are we building anything at all? What is the core purpose within us that drives us so hard to run through walls, empty our bank accounts, and set fire to our personal lives? It's not because we couldn't w...