Models write good code now. Ask anyone shipping with them. The problem moved, and most of the industry is still arguing about the old one.
Reuben Brooks put the shift in two sentences in May 2026:
"Existing models can already write almost all of your code."
"The limiting factor is whether you can know that they did what you wanted."
OpenAI reached the same place from inside their own team. In February 2026 they described five months of building a product with zero hand-written code. Here is what slowed them down at the start:
"Early progress was slower than we expected, not because Codex was incapable, but because the environment was underspecified."
And in October, Google Cloud AI Research opened the Harness² paper with a line you should read twice:
"Even a strong model can perform substantially differently under different harnesses"
Three groups, three different starting points, one conclusion. The model is not the bottleneck. The system around the model is.
I agree, and I bet the project on the idea before the industry had a word for the idea. On 31 December 2025, three MIT researchers published a paper on recursive language models: keep a long input outside the model's context and let the model work through the input in pieces, from a programming environment. Eighteen days later, on 18 January 2026, I made the first commit to Merlin. The design document lists what the system does not do. The first non-goal reads:
"Modify or retrain any LLM"
No fine-tuning. No custom model. Every improvement had to come from the structure around a model somebody else trained. The industry started calling this harness engineering in February 2026. We were already six weeks into building one.
Here is where I part ways with most of the harness work I read. The OpenAI team answered the problem with more autonomy: agents reviewing agents, humans optional in review, and as few blocking merge gates as possible. In their own words:
"Humans may review pull requests, but aren't required to."
For their goal, shipping speed on an internal product, the trade makes sense.
For mine, the trade runs backwards. If the hard part is knowing whether the work was done, then every layer you add without a check makes the hard part harder. A second model reviewing the first model adds an opinion. A rule reading the record adds a fact.
So the question I care about is narrower than how capable the model is. I want the system to show you, with evidence you check yourself, what the model did and did not do. Capability improves every quarter without any help from me. Evidence improves only when somebody builds for evidence.
Every post after this one comes back to the same split. The model proposes. Something outside the model decides what the system has earned the right to claim.