Fundraising basics

0:00 Fundraising basics

Should you raise money at all? It's the first question, and many founders skip it. They assume that raising investment is the goal, when it's really a tool, and not always the right one. In this lecture, you'll learn the main funding options and their trade-offs, how dilution works, what SAFEs and convertible notes are, what investors look for in 2026, especially from AI businesses, and how to prepare so that when you do raise, you raise on good terms.

0:35 Why it matters

Why does this matter? Because the money you take shapes the company you build. Venture capital funds need a few huge successes to make up for many failures, so they back businesses that could become very large, very fast. That pressure is great if it matches your ambition. It's painful if you wanted a profitable, steadily growing business. Other options, like revenue, loans, grants or angels, come with different obligations. Here's the key idea. Choose funding that fits the business you actually want to build, not the one that sounds impressive.

1:15 Funding as vehicles

Here's an analogy. Funding is like fuel for different vehicles. Bootstrapping, funding from savings and revenue, is a bicycle: slow, but you're fully in control and it never runs out of petrol. Loans and revenue-based financing are a car on hire purchase: you keep ownership, but you must make the payments whatever happens. Angel investors are a friend who chips in for petrol and gives directions. And venture capital is a rocket. Incredibly fast, but it only goes up, and once you've lit it, there's no gentle cruising. Grants, competitions and accelerators are extra fuel with conditions attached.

1:58 Dilution and instruments

Now dilution. When you sell shares, your percentage falls. Example, illustrative: pre-money valuation four million, investment one million, so post-money valuation is five million. The investor owns one divided by five, which is twenty percent, and founders who owned everything now own eighty. Over several rounds, dilution compounds, and an employee option pool dilutes you further. Early-stage instruments include priced equity rounds, convertible notes, which are loans that convert into shares later, usually with a discount or a valuation cap, and SAFEs, the Simple Agreement for Future Equity, created by Y Combinator and widely used in the US. Outside the US, get local legal advice on how they're treated.

2:45 Worked example 1: a SAFE (illustrative)

A simple worked example, illustrative. A founder raises five hundred thousand on a post-money SAFE with a five million cap. When it converts, the SAFE holder owns about ten percent of the company, as defined in the SAFE, before the next priced round dilutes everyone. Sounds manageable. But suppose she raises two more SAFEs over the next year, at different caps, and investors in the priced round ask for a new option pool. Add it all up, and the founders can end up owning far less than they expected. The fix is simple. Keep a cap table spreadsheet and model every SAFE's conversion before you sign.

3:31 What investors ask AI startups

What do investors look for? A large, clear problem. A strong team with real insight. Evidence, meaning traction, retention and customer commitments. Sound unit economics. And a credible plan for what this money will achieve. In 2026, AI startups also face sharper questions. What stops a model provider or a big incumbent shipping this as a free feature? What's your cost per task and gross margin, and how does it change as usage grows? How do you measure quality, and what happens when the AI is wrong? What if your model provider changes price or terms? And how much do you achieve per person?

4:16 Worked example 2: an AI legal startup (illustrative)

Now a realistic scenario, illustrative. Fatima's AI-assisted legal document review startup in Riyadh is talking to investors. Her first deck says: we use the best AI models. Investors aren't impressed. So she rebuilds her story. Defensibility: a curated dataset of Saudi commercial contract clauses and a workflow built with law firms. Unit economics: cost per document, falling over six months, and gross margin by segment. Quality: an evaluation set reviewed by lawyers, with an error rate she tracks weekly and a lawyer sign-off on every final output. Dependency: she can switch between two model providers. The conversations change completely, because she's answering their real questions.

5:02 Watch me: getting ready

Watch me prepare. I start with the investor readiness checklist from the lesson. One-sentence description. Traction by month and a cohort table. Unit economics, and for AI, cost per task and gross margin by segment. The financial model with runway before and after the raise. Use of funds tied to milestones. The cap table, including every SAFE and its cap. Legal documents, and crucially, proof that founders and contractors have assigned their intellectual property to the company. Then I build a data room: six folders on a shared drive, view-only access. And I start sending short monthly updates to prospective investors months before I ask for money.

5:49 The deck and the ecosystem

Let's talk about the pitch deck itself. A typical early-stage outline: the problem, the solution, why now, the market, the product, traction, the business model, competition, go-to-market, the team, financials, and the ask with use of funds. Keep it short, clear and evidence-led. And regional context matters. Pakistan, the UAE, Saudi Arabia, the UK and the US all have active startup support, including government-backed programmes, accelerators, angel networks and funds. Terms and programmes change frequently, so research what's current, speak to founders who've raised recently, and check the reputation of any investor before you accept their money.

6:31 Common mistakes

Now the common mistakes. Raising venture money for a business that doesn't fit the venture model. Starting too late, with too little runway. Accepting the highest valuation without reading the other terms, like liquidation preferences. Stacking SAFEs without modelling dilution. Missing IP assignments, which can stall a deal at due diligence. For AI startups, pitching the model instead of the moat. And forgetting that good alternatives exist: revenue, customers who prepay, loans, revenue-based financing and grants. Many excellent companies never raise venture capital at all.

7:08 Recap and try this now

Let's recap. Funding is a tool. Match it to the business you want to build. Understand dilution, and model every SAFE and note on your cap table before you sign. Know what investors look for, and for AI businesses, be ready with defensibility, cost per task and margin, quality evidence and provider dependency. Get your legal house in order, especially IP. Your try this now: complete the investor readiness checklist, even if you don't plan to raise yet, and build a simple cap table showing your ownership after one SAFE. Next module: legal structure and building the company.