When The Minimum Viable Product Is Too Minimal And Not Viable

All technology product development is fractally iterative, whether you want it to be or not. The agile development processes at least recognizes this. But agile, and its idea of a Minimum Viable Product (MVP), replaces the waterfall development process' requirements - which consumes a lot of thought, research, and consensus ahead of time - with a competent product manager and close proximity to the customer. My long professional experience working in both waterfall and agile processes suggests that this can work. Except when it doesn't.

This past spring I was the victim of a Minimum Viable Product strategy when I bought BMW Motorcycle's latest GPS device, the Connected Ride Navigator (CRN-1), for my 2024 BMW R1250GS Adventure, my fourth BMW motorcycle. I spent about US$800 on the CRN-1, and it was a disaster. Prior BMW Motorrad navigators were built by Garmin, and to be fair, had their own hardware issues. But this one was a BMW product, reportedly with TomTom maps. The hardware seemed pretty solid, but it was as if the software had been designed and written by someone who had never used a navigator (BMW's or otherwise), and had never ridden a motorcycle.

Besides having lots of professional experience writing code to use GPS devices and to use Open Street Maps, I had used an old Garmin standalone unit on many car trips, and my Subaru WRX has an in-dash TomTom. My basic navigation needs are simple. I want to know what road I'm on. I want to know what the next cross street is. I want to know what direction I'm going. Basic stuff like that. The CRN-1 couldn't do any of that. On a recent trip through northern New Mexico, the screen typically was all gray with a single green line - presumably indicating the road - on it; no labels, no other information. And when there were labels, the font was so tiny as to be unreadable with my old eyes using my progressive spectacles.

Here's the MVP thing: since I bought the CRN-1, there have been two software updates, and with each one the device has gotten a little better. But after the New Mexico debacle I had already bought a Garmin Zūmo XT2 navigator, a motorcycle-specific model from BMW's now-competitor, for about US$500. Since I had to modify the navigator cradle on the motorcycle for the XT2, I am unlikely to ever go back.

Sure wish I hadn't spent the money on the CRN-1. You'd think I'd know better than to buy the first release of any tech product. After all, that's why I bought the R1250GS instead of its R1300GS replacement. I'm used to BMW's motorcycle products being well designed and overpriced; the new BMW navigator got one of those right. The MVP CRN-1 was too little and too late.

Large Language Models and the Fermi Paradox

(I originally wrote this as a comment on LinkedIn, then turned the comment into a post on LinkedIn, then into a post for my techie and science fictional friends on Facebook, and finally turned it into a blog article here. I've said all this before, but it bears repeating.)

The destruction of the talent pipeline by the use of AI for work normally done by interns and entry-level employees not only threatens how humans fundamentally learn, but leads to AI "eating its own seed corn". As senior experts leave the work force, there will be no one left to generate the enormous amount - terabytes - of content necessary to train the Large Language Models.

Because human generated content will generally be perceived to be more valuable than machine generated content, humans using AI to generate content will be highly incentivized to not identify AI generated content as such. More and more AI generated content will be swept up along with the gradually diminishing pool of human content to use as training data, in a kind of feedback loop leading to "model collapse", in which the AI produces nonsense.

A former boss of mine, back in my own U.S. national lab days, once wisely remarked that this is why the U.S. Department of Energy maintains national labs: experienced Ph.D. physicists take a really long time to make. And when you need them, you need them right now. Not having them when you need them can result in an existential crisis. So you have to maintain a talent pipeline that keeps churning them out.

It takes generations in human-time to refill the talent pipeline and start making more senior experts, no matter what the domain of expertise. Once we go down this path, there is no quick and easy fix.

The lobe of my brain that goes active at science fiction conventions suggests that this anti-pattern is one possible explanation for the Fermi Paradox.