As the saying goes, the future is already here its just not very evenly distributed. Also not evenly distributed: Access to the expensive education typically needed to stand a chance of obtaining one of the jobs of the future.
Y Combinator backedLambda School is hoping to change this, with a model for funding higher education that aims to shoulder the risk burden on behalf of talented students, absorbing tuition fees until each person is in a position to pay and thereby investing in underrepresented human potential, as co-founderAusten Allred puts it.
Bothhe and his co-founder had wanted to study computer science at college but ended up dropping out because of the expense, he says. This experience gave them the idea to restructure tuition fees for remote learning starting with a computer science program but with the ambition of expanding out to teach anything and everything in future.
Asecond course, in machine learning and AI, is in the works. After that theyll launch a program teaching mobile development and one focused on bioinformatics.The careers of the future is our original niche. And then well expand out from there, he says.
Allred describes the startup as as much as a fintech play as an education play with as much finance and psychology underpinning it as technology.
Both my co-founder and I come from very conservative financial backgrounds. So were on our own to pay for college, he tells TechCrunch. We come from a really small town and we see a lot of people who are very talented and kind of underemployed just because theyve never had the opportunity. And I think a lot of people in Silicon Valley especially underestimate how big of a risk it is to go to school for four years, and put it all on student loans, and hope that it all works out on the other side.
A lot of people in Silicon Valley especially underestimate how big of a risk it is to go to school for four years, and put it all on student loans, and hope that it all works out on the other side.
The school currently offers students a six-month intensive course in computer science, with all its lectures and classes delivered online but live and interactive (rather than pre-recorded and self-paced) for which theyre asking students to pay as little as nothing up front.
If students choose to pay nothing up front, payment comes later but only once they have a job earning more than $50,000. Then they pay 17 per cent of their income for two years (up to a maximum of $30k).
Our main mission is just giving access to people for higher education and to get really high paying jobs, says Allred. The problem that weve found that we thought would be the case but it was even more so the case than we had originally assumed was if youre a code bootcamp and you charge people $10,000 up front, $20,000 up front, thats a lot of money and the vast, vast majority of people just cant afford that.
And that doesnt mean theyre any less capable or any less talented, that just means that for one reason or another they come from a situation where they dont have $20,000 in cash when theyre trying to start a new career which makes a lot of sense because they dont have a career yet.
Another difference he claims the program offers vs a coding bootcamp is being able to offer a deeper grounding in the subject matter describing the current program as closer to a CS degree than we are a bootcamp.
Though theres also, evidently, a strong focus on employability and teaching skills that are being demanded by employers now, rather than teaching theory for theorys sake. Plus students wont get any formal qualification on graduating from the program rather the pledge is theyll have the skills employers are looking to hire for.
And while there are some entirely free higher education courses available online, such as via MOOCS programs, the general difference of Lambdas proposition is to offer a more guided and intense teaching experience, with an explicit focus on employability.
Were really looking to efficiently move people from unskilled to skilled and highly valuable, is how Allred sums it up. Because we are investing in people instead of having them pay up front we can go a lot deeper and have a much longer program [than a bootcamp] and that is going to show when our students go get jobs.
It all comes down to our incentives are entirely aligned with the students and our job is to get them a high paying job. And the numbers work out essentially we can do almost anything that makes sense as long as we know that that is going to increase their prospects of getting a high paying job. And thats just a very different model than anything thats really ever been tried in education, at least in the US, he adds.
So how is the startup able to shoulder all this up-front-capital risk? Students will and do drop out, of course (the current rate is around 10 per cent, says Allred).
And even those that graduate arent absolutely guaranteed a job although he says the program has around50 employer partners actively looking to hire its students, plus its building a network that aims to offer ongoing support such as networking for its alums as they go about their job search.
Allred says its reducing risk by being selective about the students it accepts onto the program. Indeed, thats whathe describes as key to the model. Although he also accepts that trying to judge who might be a good student (and worker) vs who might not is not a hard science by any means.
If we were going based on sheer credentials its almost an inverse performance that weve seen so far, he says, discussing how selections have been going so far. So the people that we see that were like alright that person, theyve never really gone to college, they probably didnt do too well in school but theyre a hard worker and they seem really sharp those people are consistently out-performing everybody else. And its interesting to us.
Basically our current hypothesis is there are just a lot of people who are left behind for a lot of different societal reasons. And were basically investing in those people.
How exactly are they making student selections? On top of a generic application, he says the team has developed logic queries that identify somebodys proclivity for a technical career.
Its not perfect by any means but its a much better method than me or you or anybody else with all of our implicit biases, looking at an application and saying yeah this person seems legit. So were really only looking for what potential do we think this mind has to be in a technical career? And we can be blind to everything else.
They therefore avoid having any specific application criteria. And also hide any factors that could bias the selection process such as names, gender, age, ethnicity and so on.
When we make a selection thats largely based on that we find that we over index to minorities we over index to people on lower income backgrounds. And our hypothesis is that thats just because those are the people who dont have other options. So were finding people who are really, really sharp.
Were shipping people computers because they cant afford a $200 computer, let alone a college education, he adds. And sure you can take out loans, but telling somebody hey you can take out $100k of loans when they cant afford a $200 computer thats a little intimidating. Especially if that doesnt work out, youre paying that off the rest of your life thats a risk that most people cant afford to take. So were really just trying to de-risk everything and find the diamond in the rough.
The school started with a three-month test course in April but has now fully switched to the six month program and currently has two cohorts started, with a total of around 100 students in all. Its aiming to run 12 courses per year taking 100 new students every month.
The computer science program runs9am to 6pm PT, though Allred notes a lot of students also spend the night doing some extra study so its pretty much a full time plus a little bit commitment for six months. (Which does mean students still have to find a way to fund their own living costs for a full six months.)
He says its currently only able to offer the free program to students in the US, as it works on figuring out the legality around income share agreements outside of the US, but he says they should be expanding into the EU and Canada with income share agreements in the next couple of months.
Theres an insane amount of demand and were raising money in order to hire enough instructors to meet that demand, he adds. We have five instructors right now. Well be adding, we estimate, another three or four per month for the next year.
Instructors on the schools staff include former teaching staff from Ivy League institutions such as Stanford and Berkeley. But the program is structured to scale its top teaching expertise across hundreds of students simultaneously, delivered via one-to-many lectures, using additional instructors to oversee individual classes and group work (each cohort is broken down into groups of around 20). Allred says this is how its able to afford top tier instructors.
But given the school is likely to be absorbing most students tuition fees up front, theres a clear need for it to raise enough capital to sustain itself and the program i.e. until students are in a position to be able to start paying back its investment in their untapped potential.
On the funding front, Allred says the longer term plan is to raise its own fund to sustain the marketplace theyre aiming to build. Though initially they do intend to raise some VC, around demo day, so they can get on with building out course content.
We have a lot of interest in purchasing the income share agreements at the point of graduation, from investment funds and that kind of thing, he notes. So there are a variety of different ways that we could fund the company.
Eventually well be raising an investment fund specifically for investing in our students the same way any other investment fund would exist to for example to fund student loans. So well be raising some equity, a fund to be clear, but thats still a long way away.
A lot of what were interested in long term is figuring out how we can get outside money to funnel intothese underrepresented groups that dont have any other options, he adds. Right now theres very little incentive theres just not a marketplace in which you can invest in those folks.
We can show that we can take somebody from making $20k per year to making $80k per year, so thats a valuable model not only to the students, but also to society and to all sorts of other things. So there should be a market around making that possible but there isnt one currently. And so were going to create that.