I made an interesting discovery a couple of weeks ago - some models still in stealth mode were being offered for "free".
So, I pulled out some projects that had been on my "todo" list for some time, and bravely spent 5B tokens in a week (1.9B on the busiest day) on open-source projects.

* Together with researchers Marco Tiloca and Rikard Höglund, we finalized a long-standing C-implementation of the security-oriented OSCORE Multicast Notifications. ( https://datatracker.ietf.org/doc/draft-ietf-core-observe-multicast-notifications/ )
* Implemented a testbed to evaluate the draft ICMPv6-SEC (a new way of using ICMP to enable dynamic timer adaptation of the QUIC stack) and evaluated its performance over interplanetary links (e.g. Earth-Mars). ( https://datatracker.ietf.org/doc/draft-pelov-icmpv6-sec/ )
* Implemented an industrial-grade CORECONF implementation for our lab.SCHC embedded stack ( https://lab-schc.fr/ ).

But Why?
My reasons were clear - I did have projects I deem important for the community, which are being shared as open-source code. This way, people that are using it will not have to spend their tokens to solve the exact same problems. In addition, the work did require a non-trivial amount of validation and "steering" of the coding agents, which would certainly have taken a non-expert significantly more time and effort.

This is seen also with the developments in physics, mathematics, and all fields of science - thanks to AI, lost of the questions we can ask can be answered in a matter of days. Yet, the questions we can ask is infinite - so the focus now moves from the "how", to the "why" - which has always been the most important question anyway!

This is one of the key points I teach my PhD students - if I set up this experiment, if I try to answer this question - what would I do with the answer? What if things don't turn out to be the way I expect? Success, as well as failure, needs to teach us something.

We still need to learn "how" things are done. And that can be extremely challenging - and gratifying. There are many people who love playing chess and that continue competing - even though the vast majority cannot even beat the chess engine on their own smartphones!

But the real question has always been the "Why". Why do we do this?
And AI cannot help us with this one - it will make it even more important.