Don Johnson

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Very insightful read! Additive manufacturing does represent a very exciting frontier in the world of innovation and prototyping. I however wonder whether if as 3D printing technology becomes more commoditized in the near future, the ability to easily replicate prototypes given the required digital information would not leave GE prone to corporate espionage in the sense that would-be infiltrators would find it easier to steal and implement design secrets since they would just need to feed digital designs into their own 3D printers to obtain copies of the prototypes. This development would eliminate any competitive advantages in manufacturing that GE may have with regard to its machining infrastructure for various mechanical products. My question is, are the potential risks associated with the safety of intellectual property worth the incremental benefits of scaling up this technology?

Very interesting read! My main concern about the Clover Food Lab model is that cuisine may not readily lend itself to the open innovation model. I say this because different people have different pallets, hence to iterate the menu endlessly to appeal to the average tastes of various crowd-sourced tasters may have the unwelcome effect of diluting the brand and what it stands for. Attempting to appeal to everyone’s pallets runs the risk of creating menus that customers do not necessarily hate, but at the same time do not feel very strong about, since the law of averages means that the product will not oscillate too widely in one direction or the other from the mean. This might limit the brand to tourist-types who just visit to experience the “cool” innovation, but not necessarily because they absolutely love the food from experience.

On November 15, 2018, Don Johnson commented on Shooting for the Stars :

Thank you for this article. NASA’s foray into open innovation is very intriguing, given the organization’s historical context, and past fierce competition with international rivals such as the Soviet Union’s NSAU during the Space Race. I personally welcome this turn towards sharing knowledge and innovation and believe it is in keeping with the human ethos of growing and supporting each other in the interest of mutual survival. I am however skeptical about the future direction of NASA’s efforts, and whether there might be attempts to take future transformational innovations “black” to preserve NASA’s competitive advantage, denying the rest of the world democratized access to technological advances. Such a development might compromise the whole notion of open innovation, and drive humanity backwards a few notches in terms of openness to collaboration.

Great article! My main concern for the Deep Thunder solution, in addition to revenue-generation opportunities, is the feasibility of scaling. The fact that individuals in remote areas will have to be incentivized to install units means that the financial cost of scaling the network across the globe could quickly become prohibitive. With that in mind, the pricing considerations for future monetization efforts might not be economically feasible in terms of willing and able demand for the product, given its incremental benefits over current predictive solutions.

Very interest article. My key concern going forward relating to the application of machine learning in healthcare diagnostics is that: who bears the liability of malpractice relating to sub-standard diagnoses and the associated negative health outcomes? I see this as one of the key roadblocks to widespread adoption because healthcare providers will be reluctant to fully automatic the diagnostic process without 100% certainty that there is no chance of error in the AI-driven process. I fear AI will be limited to a supporting role in diagnostics for the foreseeable future. Which raises the question: what is the real value add versus current methods of diagnosis?

On November 14, 2018, Don Johnson commented on 3D Printing…we should ‘Just Do It’! :

This is a very informative article that highlights the game-changing potential of 3D printing when it comes to innovation and customization in manufactured goods. My key concern with Nike’s use of 3D technology in its R&D process has to do with the scalability of the approach given current constraints with the technology relating to speed and at times unfeasible costs. For now, this continues to be an interesting conceptual application of 3D technology, yet it remains to be seen whether Nike will be able to elevate its use to the level of scale where its beneficial impacts are felt across the spectrum of Nike’s value chain.

I really enjoyed this post. My fear is that 3D printing capabilities, like mobile phones two decades ago, will evolve to become increasingly accessible to various stakeholders. With this potential trend in mind, and with increased ubiquity in in-house 3D printing capabilities, firms like Stryker may face an existential crises as the market begins to substitute out Stryker’s services for perhaps lower quality in-house 3D printing alternatives. One way in which Stryker could counteract this trend would be to incorporate other value-added services together with the provision of its 3D printing product to add significant measurable value for clients.

On November 14, 2018, Don Johnson commented on D.E. Shaw: Double Down or Diversify? :

I am not entirely convinced that firms like DE Shaw can continue to maintain their AI-driven competitive advantage indefinitely. I say this because, as new breakthroughs in AI continue to emerge, the technology will become increasingly democratized, such that firms like DE Shaw will find themselves grappling with newcomers with superior or equal AI-driven investment strategies. With that in mind, I believe that it is absolutely critical for DE Shaw to continue to expand out its business lines by investing in developing other standalone capabilities within the investment management space, to help buttress the diversification agenda.

Einstein is an inspiring application of artificial intelligence that opens up vast avenues for enterprise customers to generate actionable recommendations from large volumes of data. I must add that although the prospect of leveraging artificial intelligence in this manner is quite appealing, it is important to continue to hone the algorithms to exclude the impacts of certain biases in their predictive algorithms that may disadvantage or attribute certain characteristics to certain customer segments that are not wholesome from a diversity and inclusion perspective. One could see how the systematic recurrence of such misattributions could become very problematic in the long term.