As Quora’s first dedicated AI agent engineer on Poe, Ma argues that AI will not make software engineers obsolete. It will make judgment, architecture and taste harder to falsify.
The fear that AI will replace software engineers has become one of the loudest debates in technology. New tools can generate code, fix bugs, explain unknown files, and build simple applications with a few lines of natural language. To some people, that sounds like the beginning of the end for programmers. Nasty Patrick Masenior AI engineer and Quora’s first dedicated AI agent engineer on Poe, it sounds like a misunderstanding of what good engineers actually do. “AI can write code,” says Ma. “But it cannot take responsibility for the system.”
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That distinction has defined much of Ma’s recent work. At Quora, he designed the first AI agent on Poe, the company’s AI product, before the software industry had reached a shared meaning for the word “agent.” He worked on Poe for more than three years, transitioning from iOS engineering to agentic AI work as the product and market evolved together. “I built agents before ‘agent’ had a real definition,” says Ma. “That meant we couldn’t rely on buzzwords. We had to focus on what the product needed to do for users.”
Poe gives users access to leading AI models and lets them create AI-powered apps and experiences. Ma was a technical lead on major Poe launches covered by TechCrunch, including Previews, App Creator, and group chats for several models. These products reflect a broader shift in software creation: more people can now describe what they want to build with a computer, even if they don’t write traditional code themselves.
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That’s one of the reasons why Ma doesn’t believe the future is just fewer engineers. He believes that the cost of building software will fall, which could increase the demand for useful software. The easier it becomes to start building, the more the industry needs engineers who can make systems reliable, maintainable and usable. “Basic software tasks are much easier now,” says Ma. “But complex software still needs architecture. It still needs taste. It still needs someone who understands what needs to be built and how it’s put together.”
This is where Ma’s position becomes more accurate than the blanket argument that AI is replacing jobs. AI can generate large amounts of code, but code volume does not equal good engineering. A system must be assessed, maintained, secured, evaluated and understood. It has to fit into a codebase that other people can work with. It must behave correctly in critical situations.
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“The best engineers will not be the ones who avoid AI,” says Ma. “They will be the ones who use it aggressively without relinquishing their judgment.”
Ma speaks from unusually direct experience. He currently writes more than 99 percent of his code using AI. That doesn’t mean he has stopped working on technology. It means that his role has shifted to directing, assessing, structuring and deciding.
“When I say I write code with AI, I don’t mean I stop thinking,” he says. “I think more about the shape of the system, its limitations and whether the output is actually correct.”
Within Quora, Ma became a champion for the adoption of AI coding tools. In April 2025, he helped transition Cursor to Claude Code, at a time when he says much of the industry had not yet recognized the wave of coding agents. His advocacy helped increase Claude Code’s usage twentyfold in two months.
That work wasn’t just about telling engineers to use a new tool. Ma also worked to make the engineering itself more AI-native. He refactored parts of the codebase and even helped redesign the engineering interview process for an environment where AI tools became part of normal development. He also drove the adoption of agent evaluation tools in the organization.
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“You can’t just give people AI tools and expect productivity to increase,” says Ma. “The codebase, process, evaluation and culture all need to change.”
That’s one of the lessons he says the industry is still learning. AI adoption can become superficial if companies only measure usage. Ma has seen how token consumption or leaderboard-style incentives can backfire. People may optimize for metrics rather than work, even setting up meaningless automated prompts to increase their AI usage numbers.
“AI usage is not the same as AI productivity,” he says. “If you measure the wrong thing, people will optimize for the wrong thing.”
The same caution applies to AI-generated code in serious systems. Ma believes engineers should use AI, but he also believes they should remain responsible for what goes into production. He has seen cases where AI pushed bad code on behalf of engineers without sufficient human awareness. This cannot be acceptable in critical systems.
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“AI agents are still early days,” says Ma. “They are not always reliable. In real systems, people still have to judge and take responsibility.”
That responsibility may become more important as creating software becomes easier. Poe’s coding agents are part of a larger movement to let users create software through natural language, but Ma believes that accessibility doesn’t eliminate the need for technical discipline.
A non-engineer may be able to generate a working app. An engineer still needs to understand whether the code is secure, scalable, maintainable, and aligned with the true purpose of the product. The difference between fast output and sustainable software still matters.
“Software is not just about running something once,” says Ma. “What matters is whether it continues to work, whether people can change it and whether the system can survive real use.”
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That belief also shapes how Ma evaluates the broader AI market. His work as a venture scout and hackathon judge has honed the way he separates useful AI projects from impressive ones. For him, the question is not whether a demo will provide surprises. What matters is whether the idea can become a system that people trust.
“Whether you’re reviewing code or reviewing founders, you’re asking a similar question,” Ma says. “Is this real? Is this useful?”
His next role will bring that question even closer to the center of AI software development. Ma joins Cognition, creator of Devin, the first AI software engineer. At Cognition, its next chapter remains within the same technical question: how much software engineering can AI handle, and what remains to be determined by human judgment?
For Ma, the future of software engineering is a higher standard for using it well. Engineers may write fewer rules by hand, but they will need stronger judgment about architecture, product value, safety and maintainability.
“AI reduces construction costs,” says Ma. “It also raises the bar for what engineers are responsible for.”
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