What I got wrong in 2025

Writing publicly means being wrong publicly. Here are the calls I made this year, graded.

1. "Local models become the default for routine developer work"#

Written in January. Partially right, wrong on scale.

Local models got much better and much easier to run. The tooling is excellent. A 14B model on a laptop is genuinely useful for the work I said it would be useful for.

What I got wrong: adoption. The default did not become hybrid. It became "frontier API for everything," because the frontier models got cheap enough fast enough that the cost argument for local largely evaporated for individuals.

The privacy argument held and drove real adoption in regulated industries, which is what I said. The convenience argument lost, which I underweighted.

Grade: C+. The technology went where I said. The behavior did not.

2. "The moat is distribution and serving efficiency, not model quality"#

Written in February. This held up well.

Model capability converged. Every frontier lab shipped comparable models. The differentiation moved to price, latency, integration, and distribution — exactly as predicted, and faster than I expected.

Google putting a frontier model into Search on launch day is the clearest demonstration. No competitor can do that.

Grade: A−.

3. "Delegated agents make review the bottleneck"#

Written in May. Correct and I understated it.

This is now the dominant complaint from every team using coding agents seriously. Throughput on generation went up several fold. Throughput on review did not move.

What I did not anticipate: the second-order effect on review quality. It is not just that review is slower — it is that reviewers under queue pressure approve things they have not fully understood, and the defect rate is showing up in ways that are hard to attribute.

Grade: A, with the caveat that I was not pessimistic enough.

4. "RAG infrastructure is solving a temporary problem"#

Written in March. Too confident, partially wrong.

Long context did get better and cheaper, and a lot of naive RAG did get replaced by just putting the documents in the prompt. That part was right.

What I got wrong: retrieval did not go away, it moved. The interesting systems now do retrieval as a tool the model calls rather than as a preprocessing step. The vector database did not die; it became something the agent queries.

That is a substantially different outcome than "the category is temporary," and I should have seen it because the same pattern — capability absorbed into the model, infrastructure repositioned rather than eliminated — had already happened twice.

Grade: C.

5. "Nvidia's roadmap slide is the real announcement"#

Written in March. Right, and the power framing was the useful part.

Power as the binding constraint became the consensus view over the year. The unit of AI infrastructure discussion is now gigawatts. Utility rate cases about datacenter interconnection are a live political issue in multiple states.

Grade: A.

6. "The npm ecosystem will have a self-propagating worm"#

Written in February, in a piece about supply chain risk. I hate being right about this one.

It arrived in September. The mechanism was exactly the one everybody had described: steal credentials on install, use them to publish, repeat.

The prediction was not clever. Every precondition was public. What I got wrong was the timeline — I expected it to take longer, because I assumed the registry would ship stronger publishing controls first. It did not, until after.

Grade: A on the call, F on the assumption that anyone would act in time.

the pattern in my errors#

Looking at the three I got wrong, they share a shape: I was right about the technology and wrong about the behavior.

Local models got good; people did not switch. RAG got less necessary; the infrastructure adapted instead of dying. Registry controls were obviously needed; they arrived after the incident rather than before.

The lesson I am taking into next year: technical trajectories are much more predictable than adoption, and adoption is where the money and the consequences are. When I feel confident, it is usually because I am reasoning about the technology, and the technology was never the hard part.