This may be controversial, but I disagree with the notion that employees deserve benefits. This is closer to a gig economy than Uber was, since these services are really ad hoc in nature. The platform essentially serves as a craigslist equivalent for labor services, and the notion that these individuals are employees of the company would be akin to ebay sellers saying that they are employees of ebay — I believe the comparison falls flat.
In terms of whether or not Task Rabbit can sustain growth, I think it relies on a larger macro trend of millenials straying away from conventional, full-time employment and pursuing more independent paths, including work on the gig economy. Since the working population of Task Rabbiters is predominantly comprised of people in their 20s and 30s, this macro trend of people seeking employment creates a natural tailwind for Task Rabbit to sustain its growth, with the unspoken understanding that the company retains its value proposition for both the laborers and the buyers.
Thank you for the fascinating article, Bernie. In the short-term, how can these applications be more commercially viable? As Patrick suggested, we are many years away from universal organs for donation. Additionally, the extent we can maximize genetically modified tissue to reduce rejection depends heavily on our understanding of genetics and biochemical interactions that are still not clearly understood. In other words, is this technology only limited by the ceiling of the biological research that underpins its commercial viability? I think the answer is unfortunately yes.
In the long-term, as Akash suggested, are there moral considerations here? At what point do we give biotechnology companies the role of psuedo-Gods in being able to re-create biological life as we know, almost at the flip of a switch of a manufacturing line. When 3-D printing becomes viable across all major organs of a body, humans may have a better quality of life, but their bodies will essentially become mosaics of produced, 3-D organs. There are moral, societal questions that come into play here.
Thank you for the read, Arting. I have a few reactions:
(1) Isn’t 3-D printing essentially table stakes for Chanel now? Other competitors will have surely caught on by this point, which compels Chanel to maintain its competitive cost position through 3-D printing. I’m not persuaded by the idea that the products will lose cache/brand, since most customers will not be exposed to those more internal operational matters, unless this somehow gets outed as a PR nightmare.
(2) Does the counterfeit question, which poses a seemingly inevitable reality that price pressures downward will depress margins, imply that Chanel should abandon its position in this medium-term? Until 3-D printing reaches critical mass among competitors, Chanel can capitalize on its healthy margins and pricing power through its cache. But would it actually be more appropriate to sell of those businesses to lower-cost competitors, so that capital can be deployed elsewhere? I personally am with you that this side of Chanel’s business seems like a short sell.
I wonder if this technology battle is a cat and mouse that should be slowed, rather than hastened, as with the rise of Jumio. Specifically, as technology for detecting fraud becomes more sophisticated so too arguably does the technology for behaving fraudulently in the first place. Jumio faces the difficult task of protecting and building upon its proprietary software, which essentially acts as a form of cybersecurity. But, as many cybersecurity firms have realized, the game of cat and mouse if never-ending, and I question whether or not the model is a sustainable one in the end, despite the growth of machine learning.
I am a big fan of Valve’s history in tapping into its communities to develop user-generated content, but am unconvinced that monetizing this user-generated content will be fruitful. User engagement keeps customers immersed in the game, but should not be exploited. Several video game companies have done this successful to date. Activision Blizzard, for instance, has a game called Overwatch, which has exploded in growth as of late, partially due to its commitment to listening to its customers. Blizzard actively sources the input of its users to make changes (sometimes dramatic), which fundamentally change the game itself. In other words, the game is a constant work-in-progress, feeding in the collective input of its users, to dynamically shape the game to the delight of its customers. With its captive customer base, it then later monetizes through more traditional means (e.g. in-game purchases). I believe Valve can take a page out of Activision’s book by creating this kind of iterative model, versus treating the problem as a dichotomy of pure user-based input or no user-based input.
Thanks for the post, Derek. As you suggest, it will be interesting to see how detailed data about specific players will influence our understanding of their strengths and weaknesses in implementing the plays that machine learning will suggest. For example, data about the physical strengths and limitations of players will imply a portfolio of physical strengths and weaknesses that might be more conducive to one play or another. Similarly, data could be used to monitor player vitals on a regular basis, so that these strengths and weaknesses are dynamic — they may change over time, which would thus impact which plays are or are not affected. This might also impact which players are best fit for a specific strategy, given those health profiles.