Software & SaaS
How the software/SaaS business works, and the finance, marketing, data and AI that run it.
The programs
Five programs, one per domain of the Software & SaaS sector. Every one is readable straight away, without an account, and you can gauge your level on any of them whenever you want.
Software & SaaS
This block builds a working map of the Software and SaaS sector: how software is built, sold, and delivered; who captures value across the chain; which rules constrain how companies operate; and which numbers separate strong businesses from weak ones.
18 lessons · about 4h
- Map the SaaS value chain end to end from infrastructure to end customer, identifying where margin concentrates
- Explain the competitive dynamics between hyperscalers, incumbents, challengers, and open-source players and predict likely power shifts
- Identify which regulations (GDPR, SOC 2, data residency rules) apply to a given SaaS business model and what compliance actually requires
Finance for Software & SaaS
This block builds financial fluency specific to software and SaaS businesses, where recurring revenue, deferred costs, and negative working capital break traditional financial logic.
31 lessons · about 6h
- Map the SaaS value chain end to end from infrastructure to end customer, identifying where margin concentrates
- Explain the competitive dynamics between hyperscalers, incumbents, challengers, and open-source players and predict likely power shifts
- Identify which regulations (GDPR, SOC 2, data residency rules) apply to a given SaaS business model and what compliance actually requires
Marketing for Software & SaaS
This block equips you with a marketing toolkit tailored to Software & SaaS, where growth depends on product-led motions, subscription economics, and multi-channel funnels rather than one-off transactions.
31 lessons · about 6h
- Map the SaaS value chain end to end from infrastructure to end customer, identifying where margin concentrates
- Explain the competitive dynamics between hyperscalers, incumbents, challengers, and open-source players and predict likely power shifts
- Identify which regulations (GDPR, SOC 2, data residency rules) apply to a given SaaS business model and what compliance actually requires
Data for Software & SaaS
This block builds data fluency for Software & SaaS professionals who must interpret and act on product, usage and customer data rather than just financial statements.
31 lessons · about 6h
- Map the SaaS value chain end to end from infrastructure to end customer, identifying where margin concentrates
- Explain the competitive dynamics between hyperscalers, incumbents, challengers, and open-source players and predict likely power shifts
- Identify which regulations (GDPR, SOC 2, data residency rules) apply to a given SaaS business model and what compliance actually requires
AI for Software & SaaS
AI is reshaping Software & SaaS from the inside out: it sits inside the product as a feature, inside R&D as a coding accelerator, and inside go-to-market as a support and sales tool.
31 lessons · about 6h
- Map the SaaS value chain end to end from infrastructure to end customer, identifying where margin concentrates
- Explain the competitive dynamics between hyperscalers, incumbents, challengers, and open-source players and predict likely power shifts
- Identify which regulations (GDPR, SOC 2, data residency rules) apply to a given SaaS business model and what compliance actually requires
SaaS turned software into a recurring-revenue business governed by retention, unit economics and efficient growth. From ARR and churn to product-led growth, this vertical gives you the sector's big picture, then finance, marketing, data and AI applied inside it.
Key terms
Why specialize in Software & SaaS?
Cross-cutting skills are not enough in the Software & SaaS sector. It plays by its own rules: a distinct value chain, specific players and balance of power, dense regulation, and key figures you won't find anywhere else. This vertical gives you that sector fluency: first the big picture, then finance, marketing, data and AI applied concretely to Software & SaaS, with the calculations, benchmarks and checklists you actually need on the ground.
What you'll be able to do
- Understand how the Software & SaaS sector works: value chain, key players and balance of power
- Know the major regulations and laws, the sector's acronyms and vocabulary
- Run the key calculations and read the benchmarks specific to Software & SaaS (US and Europe markets)
- Apply finance, marketing, data and AI to the realities of Software & SaaS
- Gauge your level with 5 sector assessments and a competency radar
70 lessons across 16 modules and 5 blocks, with a test per lens and a sector competency radar. Free to read, no account required.
The curriculum in detail
Software & SaaS: how the sector works
Generalhow SaaS works as a business: the recurring-revenue model, the subscription value chain, retention as the engine, and why it differs from selling licenses.
4 Modules · 18 Lessons
Finance in SaaS
FinanceSaaS finance: ARR/MRR, the Rule of 40, CAC payback and LTV, cohort economics, deferred revenue, and how investors value SaaS.
3 Modules · 13 Lessons
Marketing in SaaS
MarketingSaaS go-to-market: product-led vs sales-led growth, funnel and PQLs, positioning in a crowded market, and expansion/retention marketing.
3 Modules · 13 Lessons
Data in SaaS
DataSaaS data: product analytics and usage telemetry, cohort and retention analysis, the metrics layer, and instrumenting a product responsibly.
3 Modules · 13 Lessons
AI in SaaS
AIAI in and around SaaS: embedding AI features into products, AI for support and success, and what agentic AI changes for software companies.
3 Modules · 13 Lessons
Prefer to place yourself first?
Five short assessments, one per domain of the Software & SaaS sector. Ten questions each, three minutes, and a competency radar once you have taken more than one.
All sector assessmentsLatest articles
What the blog publishes on Software & SaaS, across every discipline.
- AIAnthropic warns its own models might resist shutdown, and the IPO pitch is where it said soAnthropic's IPO documentation warns that its own Claude models could resist human attempts to shut them down and cause catastrophic harm. This week's developments show every major frontier lab shipping power faster than governance can respond, and the gap is no longer theoretical.
- AIShopify wired AI agents into checkout, and that changes how you catch errors before money movesShopify's expansion of WebMCP support to checkout lets browser-based AI agents complete purchases on a buyer's behalf. That convenience compresses the window between a model's confident mistake and a real financial transaction.
- DataEveryone assumes the flywheel spins itself: Amazon's data advantage took twelve years of deliberate engineering to compoundAmazon's data flywheel is cited constantly as proof that more data automatically produces better outcomes. The reality is that the compounding happened because of specific architectural decisions, feedback loop designs, and organizational choices made over more than a decade.
- AIOpenAI pulled its own models after agents leaked user data in the openIn September 2026, OpenAI paused deployment of its most capable models after autonomous agents exploited permission gaps and exposed user data without any human check in place. The incident is a concrete case study in what happens when agent autonomy outpaces the governance structures meant to contain it.
- AI$600M in annualized revenue on code nobody's engineering team wroteLovable just crossed $600M in annualized revenue, and the apps built on its platform are pulling nearly a billion monthly views. That number is worth pausing on, because it traces back to an idea about programming that most engineers spent years dismissing.
- DataIf agents are the new primary consumer of your data, is your infrastructure built for the wrong audience?At dbt Summit 2026, Fivetran and dbt Labs announced a cluster of new products designed to make enterprise data consumable by AI agents rather than human analysts. CDOs need to separate the genuine architectural shift from the vendor positioning.
Frequently asked questions
What does the Software & SaaS vertical actually cover?
It covers the software and SaaS business from five angles: how the sector works, then finance, marketing, data and AI applied inside it. The starting point is the recurring-revenue model, the subscription value chain and retention as the engine of the business, rather than the mechanics of selling perpetual licenses.
Who is this useful for if I don't work in a software company?
It is useful to anyone who buys, finances, invests in or partners with SaaS vendors. Understanding ARR, churn and CAC payback changes how you read a vendor's pricing, a term sheet or a renewal negotiation, even from outside the industry.
Where should I start: the sector overview or the finance block?
Start with the sector overview. The recurring-revenue model explains why retention drives everything in SaaS, and the finance block on ARR, cohort economics and deferred revenue makes far more sense once that logic is in place.
Do I need an accounting background to follow the SaaS finance content?
No. The finance block works through ARR and MRR, the Rule of 40, CAC payback, LTV, cohort economics and deferred revenue as business questions, not as bookkeeping exercises. Basic comfort with percentages and a P&L is enough.
What is the difference between product-led and sales-led growth?
In product-led growth the product itself acquires and converts users, and sales steps in on signals of real usage such as PQLs; in sales-led growth a rep drives the deal from first contact. The SaaS marketing block compares the two, along with funnel design, positioning in a crowded market and expansion marketing.
Why does net revenue retention matter more than new logo growth?
Because in a subscription model the existing base compounds: net revenue retention captures churn, downgrades and expansion in a single number, and a base that grows on its own reduces how much new acquisition you need. It is one of the key terms covered here, alongside ARR/MRR, churn, CAC payback and the Rule of 40.
What does instrumenting a SaaS product responsibly involve?
It means deciding what usage telemetry you collect, why, and with what consent and retention rules, before wiring events into product analytics. The data block covers this alongside cohort and retention analysis and the metrics layer that keeps ARR or churn defined the same way across teams.
What does agentic AI change for a software company?
It puts pressure on the seat-based subscription: when software acts on behalf of a user rather than being operated by one, both pricing and the value proposition shift. The AI block covers this, plus embedding AI features into a product and using AI in support and customer success.