AI coding tools have changed the pace of software delivery. What used to take a sprint now takes an afternoon. What used to require a senior engineer to write from scratch now gets drafted in seconds with a prompt.
That's the good news.
The harder question is what happens after the code is written. Because the tools that generate code don't verify it. They don't know whether the feature they just built broke the checkout flow, corrupted a document template, or introduced a regression three layers deep in a codebase that has been accumulating complexity for five years.
Most engineering teams are navigating this gap right now, whether they've named it or not.
The Gap Isn't a People Problem
The instinct when quality slips is to add headcount. Hire a QA team. Assign someone to own testing. Build a process.
That instinct made sense in a previous era of software development. It makes less sense when the volume of code your team ships has doubled or tripled because of AI, and the economics of staffing a full QA function haven't changed.
Ron Alexssen, Engineering Manager at Counterpart, put it plainly: for less than half the monthly salary of one offshore developer, Checksum gives him the impact of a full QA team. "If I were trying to replace what Checksum is doing," he said, "it would take me at least a full team of six to ten people."
Counterpart ships to production every day. They have no dedicated QA headcount. And since implementing Checksum, they've caught at least one significant production issue per week before it ever reached a customer.
That's not a headcount story. It's an infrastructure story.
The Confidence Problem Is the Real Problem
Ask most engineering leaders what keeps them up at night and it's rarely "we don't have enough features." It's "I don't know if what we shipped is going to hold up."
That uncertainty has a cost. It shows up in slower release cycles, where teams delay shipping because they're not confident. It shows up in production incidents, where something breaks that manual testing should have caught but didn't. And it shows up in eroding trust with internal stakeholders, where the relationship between engineering and the rest of the business slowly degrades every time something ships broken.
Alexssen saw it coming before it became a crisis: "We knew that as our systems grew more complex, manual verification alone wasn't going to cut it." Checksum replaced that uncertainty with a clear yes or no before every production push.
The value isn't just fewer bugs. Engineering now shows up to company-wide meetings and reports zero production outages for the code they own. "When I don't have to report any outages, I feel like a hero," Alexssen said. "Zero is my favorite number."
What Changes When Testing Scales with the Product
One of the less obvious benefits of automated end-to-end testing is what it catches outside of engineering's direct control.
Counterpart publishes insurance contract documents. Those documents are maintained by a non-engineering team and change independently of the codebase. In one instance, Checksum caught a configuration change made outside of engineering that would have broken production regardless of what the code did. For Alexssen, that was the proof point. "That totally shows the value goes well beyond just the engineering team," he said.
That's the promise of coverage that scales with a product's actual complexity, not just its code. As systems grow, as more teams contribute to what ships, as AI-generated code fills in more of the surface area, manual verification falls further and further behind. The teams that stay ahead of this are the ones who treat testing as infrastructure, not as a task on someone's to-do list.
The Infrastructure Framing
The shift worth making is from thinking about testing as a phase of development to thinking about it as infrastructure that runs continuously alongside development. That's what a continuous quality platform provides.
In that framing, the question isn't "who is responsible for QA?" It's "does our infrastructure give us confidence that what we're shipping works?" A team using AI coding tools without that infrastructure is driving faster on a road with no guardrails.
Checksum is built for this. It generates and maintains end-to-end test suites that live in your own codebase, heal automatically when your product evolves, and give you a clear signal before every production release. It doesn't replace your engineers. It gives them back the confidence that fast-moving, AI-assisted development can quietly erode.
The teams shipping with confidence aren't the ones that slowed down. They're the ones that built the infrastructure to move fast without breaking things.
