The essay is short, confidently argued, and — perhaps unsurprisingly for a company that sells a code review product — arrives at a conclusion that happens to justify its own feature, a layered “Change Stack” view built on top of a real TanStack pull request. That commercial framing doesn’t make the argument wrong. But it does mean the argument deserves a closer look than a marketing page usually gets, and it’s worth separating what is genuinely insightful here from what is convenient rhetorical packaging.

This review does three things. First, it restates and stress-tests the core claim: that agentic coding has created an “explainability gap” between how fast plausible code can be produced and how fast humans can understand it. Second, it evaluates the proposed remedy — a multi-level, traceable “stack” between intent and code — against what actually happens in engineering organizations today. Third, it offers my own view on where the thesis holds, where it’s incomplete, and what I think teams should actually do about it, which is not quite the same as what the essay recommends.

Strip away the product framing and the underlying observation is this: a large language model can now generate a pull request that touches forty-five files, spans authentication, routing, templates, and configuration, and passes every test — in the time it takes a human reviewer to make coffee. The generation step, which used to be the dominant cost of software change, has collapsed. What hasn’t collapsed, and arguably can’t collapse in the same way, is the cost of a human deciding whether that change is correct, whether it fits the shape of the system, and whether it’s a good idea in the first place.

I think this is the strongest part of the essay, and it’s worth taking seriously on its own terms rather than as a sales pitch. There’s a useful distinction buried in here between two very different kinds of work that code review has always conflated: verification (does this compile, pass tests, follow style, avoid obvious bugs) and judgment (should this exist, does it belong, what does it commit us to going forward). Historically, verification consumed most of a reviewer’s time, simply because producing a diff that even reached the “worth judging” bar took real human effort. Agentic coding breaks that coupling.

Where I’d push back is on how novel the essay presents it as being. Large, sprawling diffs that outpace reviewer comprehension are not a new problem invented by coding agents — anyone who has reviewed a rushed migration, a vendored dependency bump with transitive changes, or a junior engineer’s first “big refactor” has felt this gap before. What agentic coding does is make the typical case look like the previous worst case. It’s a difference of degree that becomes a difference of kind once it’s the default rather than the exception.

It’s worth asking a skeptical question the essay never asks itself: is “understanding” really the bottleneck, or is “trust calibration” the bottleneck, with understanding being one (expensive) way to get there?

Here’s what I mean. A reviewer doesn’t actually need to build a complete mental model of a forty-five-file change to responsibly approve it. What they need is calibrated confidence — a belief, grounded in evidence, that the change is safe to merge, proportional to the risk it carries. For a low-stakes internal tool, skimming a diagram and spot-checking two files might be entirely sufficient. For an authentication flow touching sign-in, callbacks, and session handling — the exact example the essay uses — the bar should be much higher, and rightly so. The essay treats “understanding” as a single, undifferentiated good that should always be maximized. I don’t think that’s how good engineering judgment actually works.

The essay’s concrete proposal is a review interface organized as a stack: intent, system behavior, change layer, semantic entity, file, line — with the promise that every claim at the top remains traceable down to the diff at the bottom. The worked example, a real pull request from TanStack/cli, is genuinely well chosen. Forty-five files collapsed into six behaviorally coherent “paths,” one of which — a WorkOS AuthKit sign-in flow — is illustrated as a sequence diagram spanning ten files that no single file could explain on its own. That’s a good demonstration, and I want to give credit where it’s due: the insight that repositories organize by implementation while reviewers reason about behavior is exactly right.

The traceability requirement is also the correct design constraint. Any system that summarizes an agent’s work risks becoming exactly the kind of “polished summary that asks for blind trust” the essay explicitly warns against. The essay’s insistence that “no representation is sufficient alone” and that both zoom directions must stay open is, I think, the single most important sentence in the piece. A review tool’s job is not to replace the diff. It’s to make the diff navigable.

Where I think the proposal is weaker is in what it leaves unexamined: who generates the six-layer grouping, and how does a reviewer know that grouping is trustworthy? If comprehension doesn’t scale, and the grouping tool is itself doing comprehension work on the reviewer’s behalf, then the reviewer now has to evaluate two things instead of one: is the code correct, and is the proposed grouping an honest and complete representation of it? I’d have liked to see the essay engage with this failure mode directly.

First, the observation that “separate reconstructions make discussion harder” is understated but important. A shared, inspectable model genuinely improves the quality of the conversation that follows, independent of whether it improves individual comprehension. This is a team-dynamics argument, not just an individual-cognition argument, and I think it’s the more durable one.

Second, the framing of the diff as immovable ground truth is the correct epistemic stance. Getting this ordering backwards is one of the more common and more dangerous mistakes in this category of tooling right now.

Third, the essay correctly identifies that the unit of behavior often doesn’t correspond to any single file, function, or even service boundary. Building tooling that reasons at the level of behavior rather than syntax is genuinely hard and genuinely valuable.

Cost and latency. Generating a six-layer traceable model of a forty-five-file change is not free. It requires running additional inference over the diff, likely multiple passes. The essay doesn’t address what this costs in time or money, or how it scales to genuinely large changes.

Adversarial and careless cases. The essay’s framing assumes a well-intentioned agent producing an honest, if large, change. It doesn’t address what a review stack should do when the underlying change is actively trying to hide something, or when an agent has taken a shortcut precisely because it was optimizing for “tests pass” rather than “behavior is correct.”

The organizational question. Perhaps most importantly, the essay treats “understanding” as a property that lives inside a single reviewer’s head. But the deeper organizational question is: who is accountable for a change no single person fully understands, even with good tooling? I don’t think that question has a tooling answer at all — it’s a governance and culture question.

I find the core diagnosis in this essay correct and somewhat overdue: the scarce resource in software delivery has shifted from producing changes to judging them, and most review tooling is still built for a world where that wasn’t true. I also think the proposed direction — layered, traceable representations — is the right shape for a solution.

Where I part ways with the essay is in how confident it is that this is a solved problem rather than an open one. Comprehension is presented almost as an engineering challenge with a clean architectural answer. I think it’s closer to a genuinely open problem in applied epistemology, and dressing it up as a UI question undersells its difficulty.

If I were advising a team on this today, I’d say: adopt tooling like this, because a shared, traceable, behavior-level view of a change genuinely beats reconstructing forty-five files from scratch. But don’t let the existence of a well-organized stack change how much scrutiny high-risk changes get. Better tooling for understanding code is necessary, but on its own it is not sufficient, and the temptation to treat a well-designed stack as license to review faster rather than more precisely is exactly the failure mode a team most needs to guard against as agentic coding scales further than it already has.