Feb 16, 2026
By Aditi Viswanathan
Over the past few years, AI has forced us to rethink some basic assumptions about how engineering organizations operate, how quality is enforced, and where human judgment belongs when output accelerates.
For a long time, engineering teams optimized around one core constraint: writing code faster. Code was labor-intensive, time was limited, and headcount was the bottleneck. Teams that could ship features with fewer people and on tighter timelines had a clear advantage. AI changes that equation, not by removing the bottleneck, but moving it.
Today, generating code is no longer the main constraint. AI tools allow engineers to be far more productive and to produce large volumes of output quickly, often code that looks convincing at a surface level. A single engineer can produce more output in a day than an entire team could a few years ago. The bottleneck didn’t disappear. It simply shifted to review, testing, validation, and the harder question of whether something is truly sound or just passes inspection.
If engineering processes don’t adapt to that shift, the consequences are subtle but immediate. Codebases become harder to understand and validate, systems work reliably for routine cases but fail under edge conditions, and teams keep shipping, even as their trust in the system slips.
Quality becomes the constraint
AI makes it easy to generate large volumes of code that compile, pass basic checks, and ship. That increased output is not inherently a problem. The risk is that code can work today without accounting for the long-term vision of the platform of which it becomes a part.
Designing systems that scale is not just about handling more users. It’s about supporting new use cases, evolving requirements, and architectural decisions that can be maintained over time. That kind of future-proof thinking depends on context and experience. It demands an understanding of where the system is headed, not just how it works in the moment.
As output accelerates, these decisions get made faster and more frequently. Without strong review and architectural oversight, teams can end up with systems that appear sound now, but which become difficult to extend, reason about, or adapt later.
This is not about AI-generated code being lower quality than human-written code. It’s about scale. When output accelerates dramatically, the probability of subtle issues rises unless review, testing, and validation evolve alongside it. Code can look sound, pass checks, and still fail under real-world conditions if edge cases, assumptions, or system-level interactions have not been examined closely.
Historically, startups accumulated technical debt because speed demanded tradeoffs. Tests were skipped. Design decisions were deferred. “We will fix it later” became a rational survival strategy under real constraints. AI alters that balance. When systems can help explore architecture, generate tests, and evaluate alternatives quickly, skipping fundamentals is no longer driven by necessity. It becomes process failure.
AI-generated code is not the issue. Human-written code is not inherently safer. The risk emerges when any output, regardless of origin, is treated as acceptable without the context, judgment and architectural thinking required to build systems that scale over time.
Human review becomes the control point
At Peer AI, this shift forced us to rethink some basic assumptions about engineering workflow. Human review now matters more than ever, and every pull request is reviewed by multiple people. As output volume increases, so does the risk of subtle issues that automation will not catch. Code can run correctly and still be wrong in ways that only experience and context reveal.
Dedicated QA has become more important again, even for small teams. For a time, many organizations leaned on “developer as QA” to move faster, and that approach can work when output is constrained. When AI accelerates everything, it stops holding. QA provides the stability that keeps speed from turning into fragility.
Testing follows the same logic. In startups, tests are often the first thing to slip under pressure, even though everyone agrees they matter. AI changes that dynamic. Writing unit tests is no longer the bottleneck it once was, which leaves far less justification for skipping them. At Peer AI, we enforce test coverage thresholds across repositories, and new code is expected to ship with tests as a baseline requirement rather than an aspiration.
None of this is glamorous work. It is process, and it determines whether AI reinforces velocity or quietly undermines it.
Design and judgment don’t disappear. They compress
One of the most underestimated changes AI brings is what it does to planning and design. Earlier in my career, it was common to spend weeks or even quarters debating a single design document, and that time wasn’t wasted. Group review mattered because it surfaced blind spots that individual engineers simply had not thought to question.
That first pass now happens earlier and faster. Engineers can pressure-test designs with tools like ChatGPT or Claude before a document ever circulates, exploring architecture options, tradeoffs, failure modes, and edge cases in hours instead of weeks. By the time a design reaches team review, the work is sharper and fewer obvious gaps remain.
Sometimes I don’t ask for a polished document at all. I want to see the conversation itself, because the questions asked and the assumptions tested tell me far more about the quality of a design than presentation ever could.
Design and judgment still matter, but the cadence has changed. Teams that fail to adapt end up spending human time on issues AI could have surfaced much earlier.
Trust is the real output
One of the most effective uses of AI inside an engineering organization is eliminating the steady drain of repetitive maintenance work that pulls attention away from product thinking.
Vulnerability remediation is a good example of this dynamic. In a SOC 2 compliant organization, the work never stops. Vulnerabilities are flagged, SLAs apply, and engineers spend time fixing issues that are rarely complex but consistently time-consuming. The work matters, but it shouldn’t pull focus away from higher-value engineering.
We automated much of that flow. Vulnerabilities are pulled from GitHub, fixes are generated, and pull requests are created automatically, then routed for human review and approval. Once approved, they merge, deploy, and update the relevant tracking tickets. Human involvement stays focused on judgment, and everything else gets out of the way.
This is what being AI-forward looks like in practice: removing friction so engineers can focus on decisions that require experience and taste.
How the bottleneck shift changes hiring
If the bottleneck has moved, hiring needs to reflect that reality. We don’t run traditional algorithm interviews, because once AI can reliably perform those tasks, testing memorization stops being a useful signal.
What matters instead is whether someone can use the tools well and think clearly while doing it.
We ask candidates to build real artifacts using AI, either as take-home exercises or live. We pay attention to how they prompt, how they debug, and how they react when something unexpected happens. We also look at how quickly they can read and make sense of AI-generated code. What consistently stands out is product thinking. The strongest candidates think immediately about the user and have a clear sense of what good looks like.
For customers, this approach matters because it draws a practical line between moving fast and taking on unnecessary risk. In regulated environments like life sciences where systems must stand up to scrutiny, that tradeoff is amplified. Scaling AI responsibly requires discipline in how systems are built and reviewed, not just better tools. Speed without control simply pushes risk downstream.
For engineers, this signals that the job is evolving rather than disappearing. The work is moving up the stack toward review, architecture, systems thinking, and taste. Writing code is no longer the hard part. Deciding what code should be written still takes judgment.
The bottleneck did not vanish; it moved. Teams that recognize that shift early are far more likely to build systems they can trust over time.
