Penicillin was discovered on September 28, 1928 by the serendipitous efforts of Scottish scientist, Alexander Fleming. Legend has it that Fleming noticed an open petri dish that had failed to show bacterial growth due to a mold growing in the agar. The penicillium mold produced a toxin that inhibited bacterial reproduction.
A short 17 years later, Fleming was (jointly) awarded the Nobel Prize in Physiology or Medicine in 1945 for “the discovery of penicillin and its curative effect in various infectious diseases”.
By 1945, it was crystal clear that the discovery of penicillin was a good idea – in the parlance of the Silicon Valley, was a truly BIG idea — penicillin and related discoveries are credited with saving more than 100 million lives in the 20th century.
Which is why we, as entrepreneurs, intrapreneurs, developers, hackers, designers and investors should endeavor to work on truly big ideas, because it is our labor invested into big ideas that make the world a better place.
Discovery and Rediscovery
The antibiotic effects of penicillin were independently discovered as folk medicine and empirically harvested science — and forgotten — multiple times before Fleming’s accidental re-discovery in his laboratory in the basement of St. Mary’s Hospital in London.
Not only did Lister discover penicillin in 1870 – a 58 or so years before Fleming is credited with the same discovery, but Fleming himself pursued the purification and manufacture of penicillin in fits and starts before giving up in 1940.
Now stories of independent discovery and re-discovery in the history of science are nothing new – so what is it that you, as someone involved in innovation, can learn from my reinterpretation of medical history?
Simply put, the history of penicillin puts a lie to the most arrogant and self-serving of notions – oft repeated and tacitly accepted – that humans have any facility in determining the intrinsic quality of innovative ideas.
If we did, the venture capital industry wouldn’t be fundamentally broken.
If we did, penicillin wouldn’t have been treated as a small idea, and would have been explored and exploited much much earlier.
Good Ideas vs. Bad Ideas
In the world of technology innovation, good ideas are often referred to as BIG ideas, while bad ideas are often referred to as small ideas, as if there were two, discrete and easily identifiable populations of ideas.
We imagine one set of ideas, seen to be, very clearly to all observers, bad and small abutting a separate population of ideas, good and big. Sometimes, we make casual reference to the notion that truly disruptive ideas often appear to be cute toys, which implies that there is some overlap between good and bad ideas.
While this illusion may be how we perceive innovative ideas, not a lot in the history of innovation and technology suggests this is an accurate or usable reflection of reality. In fact, with a bit of reading, the exact opposite appears to be true – as a brief review of the history of penicillin demonstrates, we are remarkably bad at discerning the difference between good and bad ideas at early stages.
Another example from the history of medicine: Leeuwenhoek peered at bacteria through a crude version of a microscope in 1677; yet the microscope wasn’t put to wide-scale in medical research until about the 1880s.
This explains the many apocryphal stories in Silicon Valley wherein plucky entrepreneurs pitching their startups are laughed at by potential investors and potential employees; only to have the last laugh when their startup hockey-sticks to the moon, disrupts the giants of the day, and begins to rain cash into the founders’ pockets…much to the chagrin of all the people that passed on them.
For a contemporary example, read this remarkable Quora thread that demonstrates how hard it was for Instragram to hire developers.
Aside: Good investors understand this and maintain a sense of humor about it. Hence, the hilariously self-deprecating Bessemer Anti-Portfolio.
Lack of Context
Because at early stages we lack the context and data to judge innovative ideas on merit – instead, we filter them through our own cramped, personal paradigms, biases and experiences.
Contrary to popular opinion, there is nothing intrinsically wrong with filtering ideas vis-à-vis personal biases – even if you try to remove all personal biases from your life and attempt to imitate Art by becoming Spock-like – you will fail.
Ultimately, your mind will conspire against you and provide (biased) context in the vacuum of hyper-logic.
The failure is not in applying your personal filter – the failure is not admitting you have applied a personal filter and pretending to be both omniscient and hyper-rational (and not having a sense of humor about it).
So how do we in innovation-land coax ideas to reveal their true selves?
Firstly, we accept that a landscape of innovative ideas isn’t composed of two separate mountains, clearly labeled GOOD and BAD – rather, we are looking at a bi-modal distribution where most ideas are of an indeterminate quality until proven otherwise. Yes, with some confidence, we can clearly identify some bad ideas and some good ideas at the tails – but the vast majority is unknown to us until we begin to manufacture context and data through application and manipulation of the idea.
Secondly, what motivates us more: the risk of an idea being a False Negative or a False Positive?
The False Positive is what most startups fear. A startup is born with a terminal illness and has nothing to lose. Knowing you will die is liberating in that you are not afraid that an ugly but big idea will get away from you – but rather that a seductive idea that ultimately reveals itself to be nothing more than a cruel illusion.
On the other hand, The False Negative is what the enterprise is concerned about. Large organizations, especially public traded ones, have a lot to lose, and are constantly in fear of missing an idea that initially looks to be bad, but upon further inspection is a truly BIG idea.
Knowing our motivations will help determine if the decision-making processes – either formal or informal – are suitably calibrated. This means different things for startups and for enterprise level organizations. I’ll write more about this in a future post.
Written on June 20, 2013 at Callas in Budapest, Hungary.