I have now been out of day-to-day startup operations for almost two years; since my company was acquired in May 2018. In September 2019 I started a full time MSc at Imperial College London. The comparison with 9 years in startups is interesting.
The main difference is the cadence of work. Although there is a substantial course workload, everything in academia is so slow and based on the arbitrary criteria of “academic judgement”.
Academic judgment is a judgment that is made about a matter where only the opinion of an academic expert is sufficient.
Definition of Academic Judgement, Cambridge University.
Contrast this to startups where the only thing that matters is achieving product-market fit, then scaling in a manner appropriate to your business model (investor funded or profit/lifestyle focused).
This affects the cadence.
Academia is about spending time researching, getting things right and eventually publishing your results (which almost nobody reads). Think of how long this takes. Months to write a paper. Months to go through a peer review process. An unknown amount of time before anyone reads or uses your work (if ever). And that doesn’t include the time spent reading, practicing and learning before you can even think about writing a paper.
Startups are the opposite extreme. A couple of founders can build a prototype product in a few weeks, gain traction and start generating revenue within months. Iteration happens rapidly and feedback comes in constantly. I am currently writing an academic paper on data center energy and in the time it has taken me to write, read and wait for feedback from my supervisor, one of the companies I have invested in has closed a pre-seed round, made several initial hires, had early customer talks, built an initial prototype and is now in the process of closing a multi-£m seed round.
Of course, academia is supposed to be like this. Sure, things could be a bit faster, but the steady pace is a deliberate part of the scientific process. It means that things are properly tested, evidence based and trustworthy (we hope, assuming we can figure out the replication crisis).
The tech industry is also suffering from excessive speed. We have failed to understand and deal with problematic content, leaving it to a small number of oligopolies to police things for us without transparency or accountability. The investor/VC approach has also been heavily criticised for inflicting huge damage on founder and employee health.
So what can we learn from this?
The first area of relevance comes when hiring from one sector to another. People used to the academic pace – particularly those spending years on PhDs – often struggle in startups.
People who are Smart but don’t Get Things Done often have PhDs and work in big companies where nobody listens to them because they are completely impractical. They would rather mull over something academic about a problem rather than ship on time. These kind of people can be identified because they love to point out the theoretical similarity between two widely divergent concepts. For example, they will say, “Spreadsheets are really just a special case of programming language,” and then go off for a week and write a thrilling, brilliant whitepaper about the theoretical computational linguistic attributes of a spreadsheet as a programming language. Smart, but not useful. The other way to identify these people is that they have a tendency to show up at your office, coffee mug in hand, and try to start a long conversation about the relative merits of Java introspection vs. COM type libraries, on the day you are trying to ship a beta.
Joel on Software
Although I have really enjoyed studying again, as I try and get a paper published I am finding the academic pace frustratingly slow.
Context appropriate cadence matters. Startups are hard and most fail, so you have to test quickly. Academic research takes time because of the depth and complexity of many areas, combined with the rigour of peer review. Where these areas converge is a problem. Technology moves so rapidly that the academic approach is likely to break and it is frustrating to have to continually review a draft paper as things change, whilst waiting for the slower aspects of academic review. We are seeing similar things with the current virus outbreak, with preliminary papers being published with limited review.
If you have any interest in crossing sectors, understanding their differences and trying to mitigate them is important. During this time, I have tried to stay connected to the startup community by investing in early-stage startups and getting involved with specific tasks for the founders. This has reminded me of the excitement of building a startup. My role at Seedcamp allows me to see many new companies trying new things. This has allowed me to avoid getting out of touch – I can focus on studying a really interesting area for a year – then come out the other side inspired to get back involved with startups again.
This is not about one being better than the other, but being aware of the differences and how to take advantage of them. Startups are an extreme form of business not suited to most people. Academia is the same in its opposites. This is probably why most people go and work elsewhere! I’m glad to be able to experience both.