Single-pass AI code isn’t dead, but “high-reasoning” is the next frontier

There's a moment every developer knows: you ask an AI to finish a thought, it fires back the obvious answer, and you move on. That's single-pass coding, and according to CloudBees CEO Moritz Plassnig, it works fine for simple, boilerplate tasks. But push into architecturally complex or security-critical territory, and that quick-reflex approach starts to show cracks. The alternative is high-reasoning, where a model breaks a problem into steps, checks its own work, and backtracks when something's wrong, much closer to how a person actually thinks through a hard problem.

Plassnig doesn't think single-pass coding is going away. He expects it to get squeezed down to the easier end of the workload, while teams learn to route harder problems to high-reasoning models instead of assuming more reasoning is automatically better. Jackson Stakeman, who runs 'The Shop' at Sparq, agrees with the routing idea but frames the moment differently. In his view, enterprises are still early in adopting AI at scale, and most of what they need is for AI to simply work reliably on the bulk of ordinary tasks, not tackle exotic multi-agent orchestration, which he considers the long tail rather than the norm.

Stakeman's bigger worry is that high-reasoning tools let companies skip building real prompt-engineering skill, and that convenience comes at a cost: the model turns into a black box. It might land on the right answer, but it can't tell you whether a custom-built approach would have gotten there better or cheaper. He notes that in conversations with customers, it's rare to hear about an in-house LangChain-style system that's actually kept pace with how quickly frontier labs are improving their models. That gap is fueling a financial reckoning, real skepticism over whether paying a premium for reasoning-heavy models like GPT-5.6 Sol or Fable 5 is worth it for any given task.

Interestingly, OpenAI itself doesn't really use the phrase "high reasoning" anymore, preferring to talk about medium, max, and scientific reasoning tiers, even for GPT-5.6 Sol. Anthropic, meanwhile, is focused on a related but distinct issue: chain-of-thought faithfulness, or why reasoning models don't always say what they're actually thinking. And high-reasoning shouldn't be confused with benchmarks like FrontierMath, the 300-question math benchmark OpenAI commissioned from Epoch AI, even though the two overlap in spirit.

Stakeman's sharpest point may be about measurement, not models. He argues the real divide isn't between companies using high-reasoning and those that aren't, it's between teams that can measure ROI on AI spend and those that can't. Teams with clear metrics can test cheap models against expensive reasoning ones task by task. Teams without that discipline tend to chase whatever's marketed as best, right up until the bill arrives and they retreat, not because the tool was wrong, but because they never had proof it was right.