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The easy startup ideas have all been done — the ones that just required some homebrew hardware hacking or PHP dorm-room coding to get off the ground. These days, you might need multiple advanced technical degrees to accomplish something significant. At least that’s what Danny Crichton muses grimly this week, in an essay entitled “The two PhD problem of startups today.” Here’s one newsy example:
Take synthetic biology and the future of pharmaceuticals. There is a popular and now well-funded thesis on crossing machine learning and biology/medicine together to create the next generation of pharma and clinical treatment. The datasets are there, the patients are ready to buy, and the old ways of discovering new candidates to treat diseases look positively ancient against a more deliberate and automated approach afforded by modern algorithms.
Moving the needle even slightly here though requires enormous knowledge of two very hard and disparate fields. AI and bio are domains that get extremely complex extremely fast, and also where researchers and founders quickly reach the frontiers of knowledge. These aren’t “solved” fields by any stretch of the imagination, and it isn’t uncommon to quickly reach a “No one really knows” answer to a question.
Even when you try to build teams with the right combinations of knowledge, he argues, each domain is now so complex that the mesh of skills required is that much harder to achieve than previous efforts.
I partly disagree, because innovation does not map on to existing domains in such a simple way. Computer scientists in the ’60s did not expect personal computing to be a thing until the homebrewers at Apple proved it. Enterprise software industry experts last decade did not expect consumer app developers to apply their bottoms-up growth skills and beat sophisticated offerings from incumbents. I expect all sorts of arcane academic ideas to be fused with market demand in unexpected ways that break apart the models we have to day, led by people who might not check all of the boxes in traditional fields.
That includes the PhD itself and the education industry. Which is where Danny and I agree. The application of software to education has been a struggle because success requires understanding two disciplines, and he concludes that the way we learn will itself have to be broken down and reformed:
“We can’t wait until 25 years of school is complete and people graduate haggard at 40 before they can take a shot at some of these fascinating intersections. We need to build slipstreams to these lacuna where innovation hasn’t yet reached.”
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Edtech’s better future
Almost to prove Danny’s first point, some of the biggest companies in edtech today were founded by technical experts who were also university professors. Companies like Coursera are today raising their late-stage funding rounds on top of a pandemic-fueled boom for online higher learning.
But this generation of edtech unicorns already looks pretty different from anything that previous generations of education experts had imagined, as you can read an overview of from Natasha Mascarenhas on Extra Crunch. For example, Udemy was founded by a group of serial entrepreneurs, and they focused on practical skills from the start (long-time TechCrunch readers may recall our startup-focused CrunchU program with them circa 2013).
Of course, this generation of so-called MOOCs is widely seen as a limited success. In a column for Extra Crunch, Rish Joshi writes about the declining “graduation” rates that many show from students over the past decade. Instead, he sees a new wave of trends, including deeper gig-based expertise and automated niche learning, that will help anyone acquire more complex skills more quickly, at every stage of the education process. Here’s more, about the gig approach:
A potential gig economy for education created via small-group learning online would have a large impact on both the supply and demand sides of online education. Giving educators the ability to teach online from their own home opens up the opportunity to many more people around the world who may not have otherwise considered teaching, and this can greatly increase the supply of teachers worldwide. It also has the ability to mitigate the discrepancy that’s existed between quality of teaching in urban and rural areas by enabling students to access the same quality of teachers independent of their location.
Companies in this space like Outschool and Camp K12, are pre-college. But take a look around at everyone trying to teach data science, product management and other concepts that traditional industries need to incorporate to innovate more quickly, and you can see the solution that Danny hopes for starting to emerge. One day soon, you might be able to school up quickly on a new skill that you need to get a job — or a medical breakthrough.
For more on the latest in the space, be sure to check out Natasha’s second part of her survey with top edtech investors.
Planning your equity after an IPO
Do you think your unicorn employer is the next Amazon or Google? Are you ready to hold on to the stock of a potential winner through all of the ups and downs that happen to any company? If you haven’t already, consider diversifying sooner rather than later, writes startup financial advisor Peyton Carr in a series on the topic this week:
We consider any stock position or exposure greater than 10% of a portfolio to be a concentrated position. There is no hard number, but the appropriate level of concentration is dependent on several factors, such as your liquidity needs, overall portfolio value, the appetite for risk and the longer-term financial plan. However, above 10% and the returns and volatility of that single position can begin to dominate the portfolio, exposing you to high degrees of portfolio volatility.
The company “stock” in your portfolio often is only a fraction of your overall financial exposure to your company. Think about your other sources of possible exposure such as restricted stock, RSUs, options, employee stock purchase programs, 401k, other equity compensation plans, as well as your current and future salary stream tied to the company’s success. In most cases, the prudent path to achieving your financial goals involves a well-diversified portfolio.
Image Credits: Nigel Sussman(opens in a new window)
A new TechCrunch newsletter: The Exchange
In addition to the popular Equity podcast and regular appearances across TechCrunch and Extra Crunch, my colleague Alex Wilhelm is launching a new newsletter called The Exchange. It’s his weekly summary of the week, based on his daily writing for Extra Crunch and TechCrunch about tech and startup finance. You can sign up for it here. As a taste of Alex’s work if you’re not familiar, in one article this week, he took a look at the explosion in the still-new area of no code software, compiling investment activity in a space that is poorly understand and coming away with this analysis:
From this we can tell that at the very minimum, Q1 2020 VC totals for no-code/low-code startups were north of $80 million, though the real figure is likely far higher. In Q2 we can see at least $140 million in money, just among rounds that I was able to dig up this morning.
That puts low-code/no-code startups on pace to raise around $500 million at the very least in 2020. The real number is larger, and can swell sharply depending on how expansive your definition of the space is. That means that the startup world isn’t waiting for venture dollars to make their vision come true. The capital is already flowing in great quantity.
The next question is whether the startup and larger software world can make the no-code services of the world easy enough that lots of folks are willing to train themselves. The more power and capability that can be offered in exchange for learning a new way of interacting with software will likely help determine how much adoption is had, and how soon.