Starting over in the loudest month ever
I have been learning AI since 2019, but I got serious about the modern stack in the exact 25 days when four frontier models dropped. Bad timing, good decision.
The common advice is to start learning something when things are calm. I managed the opposite. Between November 17 and December 11, four labs shipped their biggest models ever: Grok 4.1, then Gemini 3 a day later, Claude Opus 4.5 the week after, and GPT-5.2 to close it out. Somewhere in the middle of that, I decided this was the moment to actually understand what I was looking at.
Some context, because “starting” is the wrong word for someone with my history. AI got its hooks into me back in 2019, and I have been learning it ever since: first the classical kind, tabular models, anomaly detection, forecasting, and these days as part of my job in automation too. And LLMs were not new to me either. I have used them since the early ChatGPT days and built a few small things on their APIs. But using is not understanding. There is a difference between driving a car and knowing what the engine does, and I had been driving for years. The degree in AI and Sustainability I am doing alongside the job kept reminding me how much engine there was.
The trigger for the rethink was small. In a study session, a uni colleague dropped an emissions dataset into Gemini 3, one we had been fighting with for a week, and asked what was wrong with it. It found the unit mismatch in about ten seconds, then suggested a check none of us had thought of. Six years around this field has given me a reliable internal alarm for oversold tools, and it stayed quiet. That bothered me more than the demo itself.
So I did what I always do when something rattles me: I went home and made a plan that was too big. Read all the transformer papers I had skipped. Close the gap between my 2019 toolbox and this. Build something real by February. You know the type of plan.
Here is what the news cycle looked like while I was making it. Gemini 3 topped the LMArena leaderboard with a score nobody had touched before. Sam Altman reportedly declared an internal “code red” at OpenAI because of it, which is a sentence I would not have believed a year earlier: the company that started the whole race, worried about being behind. Then GPT-5.2 landed on December 11 and the leaderboard shuffled again. Shelly Palmer called it an AI December to remember, which undersells it a bit. It was the most concentrated burst of capability the field had ever seen, and there I was, six years into learning this field, a daily user of these models, and unable to explain what attention actually means. Awkward.
Was that discouraging? Weirdly, no. Two things helped.
First: the fundamentals do not care about the leaderboard. Attention, embeddings, context windows, why models hallucinate. None of that changed between Gemini 3 and GPT-5.2. Learning it in a loud month costs the same as learning it in a quiet one, except the loud month keeps reminding you why it is worth it.
Second: nobody else can keep up either. That was the most freeing realization of December. The people who look like they are on top of every release are mostly skimming the same three summaries as everyone else. Once you accept that “caught up” is not an achievable state, you can stop sprinting and start walking in one direction.
So my actual restart was boring on purpose. I picked one model as my daily tool and ignored the rest. I asked it embarrassing questions I would not ask out loud. I made it explain my own data scripts back to me, then checked whether it was right (mostly yes, twice confidently no, and those two taught me more than the yeses). And I started a list of things I nodded along to but could not explain, which after two weeks stands at 23 entries. Humbling, after six years of learning this. That list is now my curriculum.
One more December item I almost scrolled past: the Linux Foundation quietly set up a foundation for agent protocols, with all the big labs as founding members. It looks like boring standards-body news. But when every rival in an industry joins the same boring standards body in the same month, someone is planning something. I have no idea yet what to make of that, so it goes on the list.
If you are standing where I stood, mid-rattle, wondering if you missed the train: you did not. The train leaves every three weeks. Get on any of them, sit down, and start with the list of things you cannot explain. Mine started with “what exactly is attention”, which seemed like a reasonable first question.