"From Pair Programming to Full Team Empowerment: How Gate Embraces the AI Development Paradigm"

Not many people know that I come from a technical background. Twenty years ago in the Asia-Pacific investment banking sector, there were probably about fifty people who truly understood algorithmic trading, and I was one of them; back then, the core of building systems was never about “getting it to run,” but rather about achieving long-term reliability under constraints such as latency, stability, monitoring, and risk control. Therefore, I was initially very averse to “Vibe Coding,” as it easily misled people into thinking that programming could be accomplished by “giving commands based on intuition.” Later, after personally using Claude Code, Codex, and Copilot to create some small software and internal tools, I realized that the issue did not lie with AI writing code, but with the misleading nature of the term: when AI is integrated into the engineering process, it resembles an extension of Pair Programming, merging “output” and “review” into the same cycle, allowing people to refocus their energy from repetitive tasks back to key decision-making and quality control. Meanwhile, reality is quite clear: for those who do not understand programming, it is challenging to produce satisfactory results relying solely on Vibe; however, for capable individuals, AI significantly amplifies efficiency and delivery quality, which also explains why one of the most sought-after “benefits” in companies has become additional LLM Tokens.

Gate’s stance on this matter is quite clear: we do not treat AI as a decorative toy but as a foundational base for the next generation of productivity. Based on this irreversible trend and in line with the mainstream consensus internally, we choose to fully embrace AI and various vibe coding tools, encouraging colleagues to continually push boundaries and drive innovation, allowing AI to truly enter the main workflow of daily development and business iteration. More importantly, we are willing to invest real money to pave this path: the company spends millions each month to enhance AI capabilities that are usable, controllable, and auditable for all employees, including more stable model access and routing, a more complete permissions and compliance framework, internal toolchains that are closer to the business, as well as ongoing training and the accumulation of best practices.

The purpose of these investments is not simply to “make everyone write faster,” but to make delivery more controllable, quality more consistent, and iterations more replicable. In other words, what Gate seeks is not scattered individual efficiency improvements but a systematic upgrade of organizational capabilities: combining excellent engineering methodologies with the acceleration capabilities of AI, enabling more people to achieve higher standards at lower costs, while reintegrating risk management, code quality, and maintainability back into the development process.

For me, the conclusion remains pragmatic: do not blindly trust “Vibe,” nor demonize AI. A better approach is to place AI back within engineering methodologies, making it a collaborative partner, a drafting tool, and an accelerator, while firmly holding onto responsibility and standards. The reason Gate is committed to investing is that we believe long-term value does not come from “looking fast,” but from “being consistently reliable and fast.” After all, in the world of finance and technology, being able to run is not difficult; being able to run stably for the long term, and being trusted to do so, is the true dividing line of professionalism.