Venture Capital and The Microeconomics of Lambda School

In my last post, I talked about how Lambda school’s innovation on pedagogy made for a solid product: scalable, effective education. Successful startups however, need one more ingredient: a business model to power rapid growth. 

Lambda isn’t the first startup to take a crack at online CS education. Nor is it the first to offer Income-Share-Agreements. What unlocked massive value for Lambda school as indicated by their recent Series B announcement aimed at expansion?

For the uninitiated, this is Lambda school: A live, fully online school that trains people to become software engineers, data scientists and designers which is free until you get a job. Instead, students pay a percentage of their income each year after they’re employed, the maximum of which is capped at $30k. More on Income-Share-Agreements here.

Let’s look at startups, venture funding and how to unlock markets by understanding Lambda school’s microeconomics. 

What is a startup?

I won’t attempt to define what a startup is. PG’s essay titled Startups=Growth encompasses the Silicon Valley philosophy succinctly, in the title itself. Startups, in this sense of the word, need to be legible to venture capital funding to fuel this growth. 

Why do startups need venture funding?

From the same essay: 

Why do founders want to take the VCs’ money? Growth, again. The constraint between good ideas and growth operates in both directions. It’s not merely that you need a scalable idea to grow. If you have such an idea and don’t grow fast enough, competitors will. Growing too slowly is particularly dangerous in a business with network effects, which the best startups usually have to some degree.

What makes a startup legible to venture funding?

Markets. Startups and venture capitalists are in the business of creating and funding outliers and having a big enough market precludes almost everything else. Growth is good but the market is paramount. Three questions determine the quality of the startup idea and its potential for scalability.  

What does the market size look like? (and how fast is it growing?) 
What portion of the total market can you capture? (and how quickly?)
What fraction of the value you create can you capture? (do the unit economics work?) 

In a post aptly titled, ‘The only thing that matters‘, Pmarca says:

If you ask entrepreneurs or VCs which of team, product, or market is most important, many will say team. This is the obvious answer, in part because in the beginning of a startup, you know a lot more about the team than you do the product, which hasn’t been built yet, or the market, which hasn’t been explored yet.
On the other hand, if you ask engineers, many will say product. This is a product business, startups invent products, customers buy and use the products. Apple and Google are the best companies in the industry today because they build the best products. Without the product there is no company. Just try having a great team and no product, or a great market and no product. What’s wrong with you? Now let me get back to work on the product.
Personally, I’ll take the third position — I’ll assert that market is the most important factor in a startup’s success or failure.

I’ll leave in his answer to “Why markets?”

In a great market — a market with lots of real potential customers — the market pulls product out of the startup.
The market needs to be fulfilled and the market will be fulfilled, by the first viable product that comes along.
The product doesn’t need to be great; it just has to basically work. And, the market doesn’t care how good the team is, as long as the team can produce that viable product.

The recipe then can be boiled down to Team + Product + Market where Market is usually the biggest determinant of outlier success.

The Microeconomics of Lambda School

I’ve talked about the Product and Team aspects of Lambda school in the previous article. Let’s look at their business model. 

The shortage of quality CS grads in the US coupled with the student debt crisis made for favorable market conditions to start code schools. The supply of skilled engineers is far too low as indicated here and in this analysis.

Price Sensitivity of Online Education

Besides having a killer team and a great product that is fully online, Lambda made important changes to its business model to unlock the full potential of the market for online education. It made the entry barrier practically zero by introducing ISAs undercutting hundreds of code schools charging anywhere between $8k to $25k, paid upfront. It de-risked decision making for students who might have otherwise never taken a chance on Lambda. 

This is how the demand curve for “online education” looks like. Lowering the price point makes it accessible to more people. So making the school free upfront was a smart move to expand the total market size. 


Trade-offs with course duration

Traditionally, code school startups offered programs varying from 8 to 20 weeks in length. A shorter duration meant student outcomes were inconsistent and enrollment suffered from self-selection of candidates. Lambda started off with a 30 week (~6 months) program which is now 40 weeks (~9 months) giving students sufficient time to become job-ready. 

I scraped some data on price and duration from Course Report. Lambda is the outlier you see at 40 weeks (I’ve included the maximum ISA cap of $30k as the price for comparison).


Code schools as a concept have existed for years. Yet no code school ever had a program that was longer than 10-20 weeks in duration. The chart shows that such programs were difficult to implement (especially with upfront fees) given the lack of a market at that price point. Growing fast in such markets is a serious challenge. The trade-off between duration and price makes all the difference in outcomes for both students and the school. 

Lambda’s 40 week program makes for quality outcomes and the zero upfront model ensures accessibility for anyone willing to enroll.

Legibility to venture funding and growth rates


Market size and the possibility of maintaining a sustainable growth rate are fundamentally interconnected. Unless you have high enough TAM, fast growth will not be possible. Insufficient growth rate, a function of the total market size was the reason why most code schools were not able to achieve meaningful scale.

There are multiple factors at play here, one of which is the price for enrollment. Other factors like mode of delivery (in-person, hybrid, online), support services (career coaches, placement programs) and course reviews had a role in Lambda’s success but price made for success at scale. 

By making the course free upfront, lambda ensured it had a large pool of applicants for all its cohorts and a sustainable growth rate. This tweet is testament to Lambda’s growth rate: from 129 concurrent students in Jan 2018 to 1000 in Dec 2018. Austen recently mentioned receiving Harvard-like application volume on Twitter for its new cohorts (Harvard gets ~30 to 40k applicants each year) which agrees well with the scalability-potential analysis discussed above. People are clamoring to get in.

Lambda’s innovation on the business model front might look like table stakes since they’ve demonstrated its viability but this was an illuminating case study on creating markets which previously did not exist.  



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