Taskrabbit CTO Scott Porad on Building with AI
"The next decade of software development won’t be defined by how much code AI can write, but by how confidently humans can ship it."
Taskrabbit is a marketplace delivering over 1.6M everyday home tasks every year, serving customers since 2008.
Seventeen years is a long time in software. In that time, Taskrabbit has grown from a scrappy startup to a complex system that thousands of engineers have touched.
We spoke with Scott Porad, Taskrabbit’s CTO, about what AI really means for mature engineering teams. His take: the next decade of software development won’t be defined by how much code AI can write, but by how confidently humans can ship it.
Here are the highlights from our conversation.
Engineers have become reviewers
For Scott, AI is reshaping what an engineer spends their time doing.
“The agent is going to write code for you - and you know it’s only 80% correct, but you don’t know which 20% is wrong.”
That uncertainty is the new frontier. As LLMs become teammates, engineers will need to get better at spotting what’s off rather than writing every line from scratch. In his view, code review and QA intuition will soon be the most valuable engineering skills.
“We’re going to have to start having pair reviewing, just like pair programming.”
At Taskrabbit, that means preparing for a world where reviewing becomes the primary learning path for junior developers. A new kind of apprenticeship where they level up by analyzing AI-written code alongside senior engineers.
Velocity isn’t about typing faster, it’s about reducing risk
For a company with a long-lived codebase, Scott says the bottleneck isn’t writing new code, it’s actually taking the time to make sure that the changes won’t break anything.
“If I could wave my magic wand and have perfect test automation, I’d take out a huge amount of risk of changing a large, complex system.”
That’s why he’s more excited about AI’s potential in testing than in code generation. Perfect test automation (unit, integration, end-to-end) would let engineers move fast with confidence.
“We spend so much time trying not to make mistakes because we don’t totally understand our systems.”
In other words, the real productivity unlock will come when AI makes every change reliable.
Assisted engineering, not autonomous engineering
Despite the hype around “AI agents,” Taskrabbit is taking a measured approach. Today, AI is most useful in assisted engineering - helping with documentation, small refactors, and, especially, code review.
Scott points to a recent OpenAI study comparing two LLM roles: a “coder bot” that wrote code, and a “manager bot” that reviewed it. The reviewer was consistently more effective.
“The technology is better at detecting whether a pattern someone is following is good, than originating that pattern from scratch.”
That insight has shaped Taskrabbit’s strategy. Instead of expecting AI to build entire features, they’re building workflows around AI reviewers: tools like CodeRabbit (no relation to Taskrabbit) that spot issues faster and surface context before merge.
The rise of “satellite apps”
For now, legacy codebases still limit what AI can safely touch. But Scott sees a clear on-ramp: “satellite apps.”
These are small, isolated tools that don’t depend on the core system: a new consent form, a marketing landing page, an internal admin dashboard.
“Someone was able to get something like Lovable to build a disclosure form in a completely automated fashion - and got it pretty close.”
For teams working in older stacks, satellite apps are the low-risk sandbox for experimenting with AI. They let you test workflows, measure time savings, and build confidence before expanding into production systems.
Culture before code
When it comes to adoption, Scott’s playbook is simple: don’t mandate - motivate.
“I encourage a lot. I don’t mandate.”
He compares it to the arrival of IDEs years ago: some engineers resisted, but once they saw the speed and clarity benefits, they never looked back.
Taskrabbit’s approach mirrors that history. Rather than top-down enforcement, they’ve formed small eval groups: engineers testing tools, comparing results, and evangelizing what works.
Put simply, adoption grounded in curiosity and credibility, not compliance.
Redefining apprenticeship in the AI era
Scott acknowledges a looming problem: junior engineering roles are disappearing. If AI writes most of the boilerplate, where do new engineers learn the fundamentals?
“The only way they’ll get to be good reviewers is to do it with people who are better reviewers.”
That’s why he envisions a new kind of mentorship, one centered on reviewing AI output instead of writing greenfield features. Peer reviewing will become the new pair programming, and the ability to critique machine-written code will define the next generation of software craftsmanship.
The long view: software that builds software
Zooming out, Scott sees AI as the start of another industrial shift.
“We just went through an information revolution over the last 30 years. Now we’re going through another one.”
This next revolution will reshape what it means to build. Engineers will move up the abstraction ladder: designing workflows, validating AI output, and architecting systems that learn from every deploy.
We had such a great time jamming with Scott! We’ve been having similar conversations with product and engineering leaders at top engineering orgs (like HeyGen, Wix, Vanta, etc) to unpack how they actually build with AI. You can find previous episodes on YouTube.