Thanks for shedding light on this opportunity – it makes so much sense for the military given their unlimited budget yet serious space constraints. As Lebron raised, I’d be concerned about power outages and mobility of printers, but barring those concerns, this seems like a no brainer for military applications.
Opposite from Sam above, I actually imagine that consolidation might further encourage this kind of innovation rather than limit it. I’d worry that competing organizations might not make this investment, but unified ones would as they’d see the benefits applied across a much larger set of forces.
Democratizing the idea generation process is a great idea – as we know from class, higher variability in early idea generation is a benefit to the system, as only the good ideas will remain, and the cost of a new idea is rather low. I think that while there are certainly questions around commitment and success, I would use open IDEO for the biggest and most revolutionary ideas and topics. Improving existing ideas incrementally can likely be done sufficiently by IDEO staff. Revolutionary ideas may lack someone committed to execution, but that’s what IDEO’s army of consultants is for!
Love this! Fascinating how Volition is inspiring its customers to drive innovation, which helps them better tie product development to customer demand versus forecasting. I think your ideas on how to further motivate and inspire innovators are fantastic. Increasing incentives will be necessary as the company scales and expands to more retail outlets. I also like how this company can easily scale globally to new markets, as it will only make products that are relevant to that market based on innovator ideation and demand.
Great work, Peter, thanks for sharing!
I was first struck by Bytedance’s shrewd business decision to partker with smartphone manufacturers to pre-install the apps. This allowed them to greatly expand their userbase and develop the dominant position.
I agree that there is potential to use this AI capability for more societally beneficial means in an enterprise setting, but I wonder if more educational content is actually best recommended using the same features the algorithm currently uses for entertainment content, or if you’d need to develop new variables.
The fact that they are employing human editors to track sexual/violent content shows how far AI still has to go before it can be 100% trusted. But I applaud Bytedance and all they’ve accomplished so far!
Awesome article! Fascinating how they built this business.
Related to @kgf above, I worry about the narrowing effect, that with too few data points the algorithm will provide bland recommendations rather than bold new style choices. It’s for this reason that I doubt machine learning can be truly creative, and why human intervention is needed.
Relatedly, I think starting from scratch in the UK is the best way to build up a sufficient data set.
I also might consider ways the company could increase its data points on style preferences. What if they integrated a Pinterest-like feature, enabling users to like or pin or upvote styles they enjoyed? This might give the algorithm a sufficiently large data set to better recommend with more nuance.