2026 Half Year Check In
We survived... so far.
This is a half year check in on the AI journey. I and some team mates predicted that 2026 was going to be a very bumpy year... and here we are. First half of the year was a lot of experimentation and testing, to validate the reality of the situation that AI models plus coding harnesses could provide real, valuable production code. That was quickly validated, and set off quite the firestorm we just lived through. Like many of you I then worked to the point of exhaustion on a backlog of projects that I could finally get off the ground.
Ive revamped my website two or three times, done some projects for the college, launched a substack (as one does). Adapted to get as much value into and out of the work and home projects as I can. I vibecoded a couple of prototypes at work that are bringing real value at least to me, and am building a work personal assistant with team members.
On a personal level, as someone with ADHD and a technology generalist mode of operations, have an assistant who can do almost anything I can think of, finish the messy detail work, and fill in gaps a specialist would know has been a key unlock. on a fun level, this is about on par with where I was in the early 90s experimenting in the early days of Windows NT, networking, and Linux. If it werent for all the anxiety I would be full on having a blast.
We quickly realized what doesnt work, slop code, trying to one shot things without testing or strong eval harnesses. this is a well known story by now. AI writing needs a lot of work, tried it, rethinking it, now that I know how its done, I see it everywhere and that its fairly valued as low value. A tougher realization is how much foundational work has to be done to allow AI to provide value at a team level.
Focus now is regularizing our APIs and data sources which we treat as real source of truth anchors, and allow trusted agents to move fast between. Right now getting HW resources is a problem, but we are betting on owning as much of the AI inferencing and training stack that we can. We are building on tool gateways, including the excellent Context Forge (https://github.com/IBM/mcp-context-forge) which we have up and running. We are also working on a model gateway based on liteMaaS(https://github.com/rh-aiservices-bu/litemaas) from Red Hat.
Whats next? I dont have a crystal ball and distrustful of people who say they do. But im expecting big discussions around build vs buy, throwing away vs fixing legacy codebases, sovereign inferencing stacks, making inferencing stacks cheaper, and continued acceleration as we learn the new tools. Stay frosty and have fun.