Interesting and well-formulated. I particularly like the discussion of the supply chain in a constrained context. Yes, Additive Manufacturing enables simpler supply chains and faster assemblies, but you consider two important points: 1) AM can eliminate supply chains as repairs to broken parts can be done using AM, and 2) there may still need to be a supply chain in order for raw materials to be shipped to the consumer, thereby eliminating the supply chain advantage. The best solution to #2, to me, is to use existing materials on Marine Corps uniforms / trucks or raw materials from combat zones, as the raw materials in a 3D print.
Interesting read and you raise great questions. To me, the economic viability of crowdsourcing is still not apparent. First, the future cash flows are diminished if a Company must share its royalty stream with the crowdsourcee. This is particualry true in the pharmaceutical industry where high, volative upfront R&D costs are paid back over a long, stable period of time. Second, the upfront costs, as you mentioned, are high to get a Crowdsource program up and running. Finally, that leads me to believe that if we expect pharmaceutical companies to continue to research and produce drugs, some sort of government intervention will need to occur – a company will not rationally pursue negative IRR projects.
Thanks for the post, very interesting. From working with former Bell Labs employees in my time pre-HBS, I agree that the Bell Labs culture + talent level was a smart way to achieve “radical change” within an otherwise sluggish Company. Nokia’s divestment of legacy telecom assets, and more, has likely allowed them the opportunity to pivot. However, I’m not sure Crowdsourcing is the best way to determine a future strategy. Moreover, this model looks like Nokia is just playing Venture Capitalist – what real synergies does Nokie provide its entrpreneurs? What is the discount rate associated with that investment? Is this an example of the “conglomerate fallacy” at work? Thanks for sharing.
Goooooaaaallllllllll! You definitely scored with this one. While I agree Nike’s use of 3D printing is important to maintaining its edge as the perceived leader in technological innovation (e.g., R&D lab), I don’t think 3D printing has the potential to save the Company Cost of Goods Sold at scale. The customization and advanced engineering that AM fulfills is just not present at Nike. Moreover, I believe that Nike’s market positioning / brand equity is so well entrenched with their target market that Adidas will not be able to out-class Nike simply because of a foray into 3D printing.
To me, the Defense Innovation Unit, Experimental (DIUX) victory in June 2018 is the single biggest indicator of Uptake’s potential. As a Company ~12 months removed from their Series D round, Uptake should be focused on securing long-term, cash-flow positive contracts — the Department of Defense is the ideal customer from this perspective. Given the knowledge Uptake will gain working with the DoD, I would advise they seek to be the dominant force in the Industrials (i.e., Defense, Aerospace, Chemicals, Metals & Mining, Packaging, etc.) sector. Retail and Healthcare both seem to be risky, and concentrated, sectors to penetrate next. With regard to Retail, Amazon et al already have a tremendous amount of data and engineering skill that make it look like the clear winner in Machine Learning Retail. Moreover, secular headwinds in Retail make it a very risky proposition. With regard to Healthcare, I worry that the complexity and ever-shifting nature of Healthcare regulation in the United States and abroad make this end-market a difficult to win space – healthcare customers tend to be of poorer credit quality. You mentioned IoT and I believe that working with Telecom companies to be the Machine Learning player in the next wave of 5G with telecom companies is a better risk-adjusted bet if the Company must find an adjacency.
You raise a handful of questions vis-a-vis Drilling Info’s competitive advantage in the space. The customer promise of their tool, DI Transform, seems particularly promising. As an outsider to the Oil & Gas industry, I often associate success in the space with a few representations, namely: “cowboy wearing wild-catters” and those blessed with capital. This combination of capital coffers and proclivity for risk seems, to me, to be the hallmark of Oil and Gas in the United States. Drilling Info, and the rise of machine learning in Oil & Gas more broadly seems to present an opportunity for a few shifts: 1) the democratization of participants in the Oil & Gas industry (i.e., as science becomes more pronounced, the source of differentiation for capital providers and risk takers diminishes), and 2) stabilization of oil prices due to better information as to what amount of oil is in any given well, the cost savings afforded by these processes, and the stability that the aforementioned #1 provides. You note above, that Drilling Info “not only reduces the time that would otherwise be required to pick intervals by hand on a series of well logs (hours or days by hand), but also increases accuracy by reducing the error inherent in visual inspection of well logs by human” – the idea that Machine Learning could save unnecessary exploration and costs suggests that Machine Learning could help the Energy industry better control climate change.