The Transformer is the neural network architecture introduced in Google's 2017 paper "Attention is All You Need" that now powers virtually every modern foundation model. It replaced earlier sequence-processing approaches (RNNs and LSTMs) and underlies GPT, Claude, Gemini, Llama, BERT, T5, and others. Its core innovation is the self-attention mechanism, which allows the model to consider all positions in a sequence simultaneously rather than processing them sequentially. It's the architectural breakthrough that enabled the modern AI revolution; understanding it (at least conceptually) is foundational vocabulary for anyone in tech.
The pre-Transformer era:
RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Me...
How startups end (and what determines who gets what). This cluster covers the major exit paths (IPO, acquisition, SPAC, direct listing), deal structures and terms (LOI, definitive agreement, earnout, holdback, reps and warranties), the rights that affect exit outcomes (drag-along, tag-along, ROFR, lockup), and the mechanics specific to exits (liquidation waterfall, exit multiples, QSBS). 26 entries.
Exits are the moment when years of equity decisions become real money. Founders should know this vocabulary years before they need it.
A business cofounder is the founding-team member responsible for non-technical functions: customer development, sales, fundraising, business model design, go-to-market, recruiting, and operations. They often (but not always) serve as the CEO, hold founder-level equity (typically 25-50% in two-founder teams), and bring skills that complement the technical cofounder's product-building capabilities. The role is controversial in startup discourse because the value-add is often less visible than a technical cofounder's "they built the product" contribution. It is the most-debated cofounder role in startup culture: dismissed by some as the "idea guy" or "BizDev person" who isn't actually building anything, defended by others as...
A problem statement is the pitch-deck slide and underlying narrative that defines the specific customer pain a startup is solving and why it matters now. It names who has the pain, how widespread it is, how painful it is today, what workarounds the customer currently uses, and the timing case, typically delivered as the second or third slide of a pitch deck (after the title slide and sometimes the elevator-pitch slide) because it frames everything that follows. It is the slide most founders underweight, and the one most investors use to decide whether the rest of the deck is worth reading before they ever reach the [Traction Slide].
The components of a strong problem statement: the specific customer (who has this problem, ...
Industry analysis is the systematic study of an industry's structure, dynamics, competitive forces, value chain, regulatory environment, technological trends, and macroeconomic factors. It's used to inform strategic decisions about market entry, positioning, business model, and competitive strategy. The most-used frameworks are Porter's Five Forces (industry structure), PESTLE (macroeconomic factors), and value chain analysis (where value is created and captured). The discipline is more relevant at strategic inflection points (founding, market entry, M&A, major pivots) than as ongoing operational practice. Industry analysis provides the macro context within which business strategy operates.
The standard frameworks:
Po...
Customer onboarding is the structured process of getting new customers from signup or contract signing to first meaningful value. It encompasses technical setup, initial training, key feature adoption, integration with existing workflows, and the establishment of usage habits, with the quality of onboarding being one of the strongest predictors of long-term retention, expansion, and word-of-mouth growth. It's the most-leveraged investment a SaaS company can make in retention; bad onboarding is the most common cause of preventable early churn.
The onboarding playbook by segment:
Self-serve SaaS (low touch):
Marketing analytics is the discipline of collecting, measuring, and interpreting marketing data across channels, campaigns, audiences, and customer journeys. It informs budget, creative, targeting, lifecycle, and product decisions, executed through web analytics, product analytics, ad platform reporting, attribution tools, customer data platforms, and the modern data warehouse. It is the function that turns the firehose of marketing data into decisions a leadership team can act on.
The modern marketing analytics stack typically combines: web analytics (Google Analytics 4 as the default since Universal Analytics sunset in 2023, with alternatives like Plausible, Fathom, Matomo for privacy-leaning teams), product analytics ...
A performance review is a periodic structured assessment of an employee's performance against expectations, conducted by their manager. Also called performance evaluation, performance appraisal, or annual review. Some companies add input from peers and direct reports via 360-degree review. Used for compensation decisions (raises, bonuses, equity refresh), promotion discussions (level changes, expanded scope), and developmental feedback. Most performance reviews are too infrequent (annual cycles miss most of the year's performance) and too vague (ratings like "meets expectations" don't drive behavior change) to actually accomplish their stated purpose. It is one of the most-implemented and least-effective HR disciplines at...
A foundation model is a large-scale AI model trained on broad, diverse data and designed to be adapted to many downstream tasks. Adaptation happens via fine-tuning, prompting, or API access. The term was coined by Stanford's Center for Research on Foundation Models in 2021 and now describes GPT-4, Claude, Gemini, Llama, Mistral, and similar models that form the base layer of the modern AI stack. The foundation model is to AI applications what AWS is to web applications: shared infrastructure that powers everything built on top.
What distinguishes foundation models:
Scale: hundreds of billions to trillions of parameters. Trained on hundreds of billions to trillions of tokens of data.
General-purpose training: trained on broa...
Round size is the total capital raised in a financing, determined by balancing runway, milestones, dilution, and capital efficiency. Key factors include runway needs (typically 18-24 months of operating cash), capital required to hit milestones for the next round, dilution tolerance (more capital means more dilution at given valuation), valuation impact (very large rounds at high valuations create future pressure), and capital efficiency (raising more than needed creates "fat" operations). Right-sizing is one of the most-important fundraising decisions and one founders frequently get wrong by raising too much (excess dilution, future pressure) or too little (insufficient runway, premature next-round fundraise). It's the dial that...