Luis Valles

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I think the business risk you mentioned around IP is a significant one for two reasons. The first reason is the straightforward case where you get into a legal battle between who owns the rights of whatever pharma product get’s produced and if it’s already in production the risk of paying out royalties that decrease profit margins. The second reason is reputational. If your business is known for being the company that steals ideas and takes advantage of scientists, then people will stop contributing their ideas to the open innovation programs. A secondary risk that comes to mind is that essentially by outsourcing part of your R&D to the public, you become reliant on them to define your product pipeline in the future. In an industry with high regulations that drive long lead times from conception to product shipment, not having a steady stream of ideas to test can slow your growth down significantly. With that in mind, I think it makes sense to think of open innovation as almost a new capability that you have to build, like learning how to engage different stakeholders to ensure that they are participating and support the open innovation ethos.

On November 16, 2018, Luis Valles commented on Space Wars advancing additive manufacturing? :

To address your question and react to John’s comment, it seems to me that there are two potential use cases for applying the 3D printing technology to space exploration. The first is manufacturing certain components used to build parts of the ship or tools for the mission. The second is to manufacture those components in space. I think in the short term the benefits of developing this technology for manufacturing on earth is very clear: they reduce costs vs traditional manufacturing methods and decrease the speed of production via easy iteration. For me, that’s enough reason to invest in the technology today. As John mentioned above, the long game would be to make it viable in space with the raw materials available off earth.

On November 16, 2018, Luis Valles commented on How Will 3D Printing Impact Sonova? :

Like others have commented above, I think it was a great point to call out that 3D printing technology is not enough to stay ahead of the competition. While this company is the market leader from a 1st mover advantage, the technology itself will be easy to replicate not just by competing manufacturers but by their customers as well, like the doctors who could just buy a 3D printer and manufacture the pieces themselves. Thinking about the question posed on how they could retain a competitive advantage made me think to the first point made in the article: “this form of manufacturing works best in areas where unit customization is vital (versus mass produced units), however, 99% of all manufactured parts are standard and lacking the need for customization.” Just how high customization is a constraint necessary to make the application of this technology economically viable, I think that defensibility from competitors (including your own customers” will need to become a second constraint to make this economically viable in the longer term. They could further segment their target customers to smaller medical practices that might not have enough capital to invest in their own 3D printing machine or multi functional clinics that don’t have a large volume of hearing aids needed. Or as you mentioned they could focus on building a specifical capability, whether in designing complex shells or the hearing technology in the shell itself. One last alternative, is that they could try to differentiate themselves like companies that sell commodities do – by brand message, reaching large scale so that the volume produced decreases, providing services around the actual product, etc.

On November 16, 2018, Luis Valles commented on BenevolentAI: From Machine Learning Startup towards New Drug Producer :

I think the question you posed around talent retention for these very technical roles that are in demand does seem important. Especially because some of these technical problems they would be working on are not simple and would probably require continuity. Because the job demand in AI applications is exploding, if they want to retain their top talent they have to incentivize them to stay beyond giving them a “difficult problem to solve” because it seems they could find a different problem to solve that is just as difficult for more pay at a different company. For pharmaceutical companies, I think they could work at aligning the technical work to the larger mission of creating drugs that presumably solve some medical problem and reduce suffering in the world. This could be a compelling reason for certain candidates. In addition to that, it seems like their machine learning work is a bridge between academic life and corporate life. For researchers who enjoy their academic work but simply want to move towards the application of their theories and higher pay, this job role would be a good fit. I think the problem of good talent changing jobs may actually be a broader trend in our generation. In the AI community it is more pronounced because the pool is smaller than other fields. From that lens, I think that companies really need to define their mission in a way that is compelling because when someone has to choose where to work I think they would rather pick somewhere that is actually trying to make a difference in the world vs just trying to make a profit.

On November 15, 2018, Luis Valles commented on Emotionally Aware Vehicles: The Future of Road Safety? :

Reading this raised a couple of interesting questions for me like, if a vehicle is emotionally aware of it’s driver, what type of interventions will this trigger? I imagine a scale of increasing invasiveness. On the low end it could simply be an automatic response from the car to raise awareness to the driver, like when your car beeps when you start driving and are not buckled. On the high end the car could simply turn off if it registers that it’s driver is alcohol-impaired above some threshhold. The further up this scale you are I think the more critical it will be to ensure that the algorithms are accurately assessing the drivers.

I think your point at the end to focus the rollout in the US to develop it’s algorithm first before expanding was a good one. There probably is a product development tradeoff between expansion vs focusing. On the one hand, geographical expansion will give you access to a much larger dataset, which is needed for these algorithms to develop statistically significant insights. On the other hand focusing in the US will ensure the quality of the data is more uniform. If you aggregate emotion data from across the globe it may actually make your predictions worse. Expressing anger in one country may look very different in a different country because of the way those emotions are conveyed. Or the baseline intensity for these emotions may be different in different cultures.

I agree with much of what’s been said on the first question – that the surplus of funding they received probably pushed them to be very ambitious with growth. Had they not received that funding, that expansion strategy would not have been accessible to them.

I don’t believe shifting the timing of the their launch to a post recession period would have affected their success. My intuition tells me that launching close to a recession vs further out would impact the funding of business operations or the purchasing of their service because I would imagine during a recession there would be less money to go around. However, it sounded like they 1) received a lot of funding and 2) they were stocking the shelves of their customers (Target, etc). To me it sounded like their biggest problem was the product fit with the market. To me this raises broader questions around how can you harness the benefits of open innovation in a way that is profitable? The platform they built seems to have done a good job of elevating product ideas that may have been more novel and outside the box than something generated by individual companies with a more developed playbook. This probably happened much faster as well because of the crowdsourcing scale effects. A big learning from this company is that it may not be good enough to bubble up the innovative ideas, you will still need to filter and assess those ideas by economic viability and consumer demand.