I never thought we’d one day have the ability to print buildings. But now that the technology is here, we should look towards the implications that this would have in the developing world. If we can use this technology to build low cost homes in developing nations, we can change the lives of millions without access to proper shelter. However, my biggest concern is obviously safety. Because the technology is so new, there is no good data on the reliability of these 3D printed construction projects. I’m no civil engineer, but I’m assuming these buildings will be much more susceptible to collapse in the event of an earthquake or other large weather events. However, I realize that this is just the beginning.
Right now, the company should focus on optimizing the safety and cost of construction. By becoming the industry leader in construction, they can set the standard (literally) for 3D printed houses. Once the market has been developed, and we begin to see more and more printed buildings, the company can expand the breath of their organization into construction materials and work towards integrating their supply chain.
Interesting article regarding the future of orthodontics. Basically everyone these days has had some sort of braces. I’m led to believe braces may now even be covered by dental insurance. When you think of the brand name for clear orthodontics, you think Invisalign. Invisalign has a clear first mover advantage and brand name recognition in this space. Hence, this is partially why they’ve been able to be so successful.
But, while it has the advantage, Align is not being complacent. They’re spending millions on research and development, and the sheer magnitude of their R&D program implies that they will maintain their competitive edge as they develop new materials to print Invisalign trays in a single step. With regard to data, I think of it as inventing the lightbulb. By this point, they probably have a good idea of what doesn’t work. With tons of customer feedback, they can optimize the strength and durability of the product while decreasing the time required by the patient. Overall, I’m optimistic about the future of Align because they capitalized on the first mover advantage and are focusing their efforts on Kaizen.
This concept brings me back to my entrepreneurial days when I was designing an app for the Apple App Store. The process is rigorous. If your screenshots don’t look good, they reject you. If your app doesn’t meet the incredibly vague community guidelines, they reject you.
After one or two rejections, the developer becomes increasingly discouraged. I’m worried that this will happen to idea-creators in LEGOs open innovation platform. Why does it matter if the idea isn’t accompanied by perfect photos? By having such a restricted process such as the 10k requirement, the barrier to review is most likely preventing good ideas from entering the system.
Finally, I can’t see this really paying off for the idea-creators. The 1% royalty seems a little low. If LEGO expects me to spend several days developing a concept and generating metadata for the concept, I would hope to be compensated. The small “potential” return is not very motivating.
I really like the idea of Quirky, especially the concept that the idea contributors receive royalties based on their idea. However, I agree with you that manufacturing the products and holding the inventory themselves was a bad move. Big manufacturing companies, like Johnson&Johnson should be able to post on the Quirky app (for a fee) and learn how to think like a consumer. Based on the influence point systems, Johnson&Johnson could even use this as a recruiting mechanism if a particular “professional” comes up with consecutively good ideas.
We see a similar point system on Quora – points based on the quality of responses. However, I struggle with the concept of allocating royalty based on idea “contribution.” I don’t think ideas are developed linearly. For instance, one person may have a great idea and then the next person may slightly build off that idea. How do you allocate points in that situation? 80/20? Seems like theres a lot of room for royalty debate, which could lead to litigation. Otherwise, I see potential with the Quirky product. Now, they just need to learn how to operate more efficiently.
Really interesting article! As I read, I consider all of the possible uses for this technology. Could Affectiva detect when a driver is under the influence? If so, could Affectiva have the ability to prevent the car from starting? I see a lot of approaches that this company could take to increase road safety. However, I do think that people would be reluctant to have a emotion-reading sensor in their car. And, if this becomes a legal requirement, the population may be skeptical of “big brother.” While I think that there will be human drivers on the road for quite some time, cars will become more and more autonomous. If humans are no longer driving cars, this technology may become obsolete quickly. Nevertheless, advances in road safety should be aggressively pursued and I’m glad Affectiva is putting their technology to good use.
Mike – really enjoyed reading about submarine hunting. Regarding the questions you’ve posed, I have two comments. First, the Navy will need to motivate more young men and women with coding skills to join the US military. A possible approach to this could be to partner with an AI technology company and offer subsidized student loans for students who are interested in a career/building skills in AI. Second, this is currently a big data problem. It requires 900 GB/flight to sweep the ocean and I’m assuming a decent amount of that data is noise. However, to play devil’s advocate, I do see some advantage to processing the data at sea. By historizing the data and then sending the data packs to shore for human analysis, I assume the US military is sacrificing on response time. Could the US Navy eventually have unmanned ships that automatically respond to high interest signals? Just my two cents.
However, as I read, I begin to think of highly backed startups which have failed. For instance, Yik Yak, Beepi, Quixey. As I think about the stages of investing, from VC to growth equity to more traditional private equity, I often associate the risk at each “stage” with the amount of information available. If The Machine makes investment decisions based on data such as industry, founder, round and investor data, is that not the same as making investment decisions based on “past performance?”
The competitive advantage of The Machine is its ability to process massive amounts of information. But, if the information doesn’t exist, I am skeptical that The Machine can accurately predict future success. Therefore, I would use The Machine in later rounds (Series B, C, etc.) and keep the “gut” in angel investing.
Right now, supervised learning machines are only as good as the information fed to them. While that could change in the near future, the human investor’s ability to gauge the idea, motivation, intellect, and capability of the startup’s CEO is the key to finding a unicorn.