Boost efficiency by 10 times: AI revolutionizes software development, and these five experiences are the key dividing lines.

AI is changing the underlying paradigm of software development.

We are in a technology-driven paradox: AI tools are accelerating the development process at an unprecedented speed, but they also expose a huge gap in capabilities - for the same tools, the output differences between different teams can be ten or even a hundred times.

This means that the so-called "AI-native development" is far from simply integrating tools into the process. Instead, it is a reconstruction of the entire R & D system: from prototyping, collaboration to deployment, every step needs to be redesigned for the involvement of AI.

In this report on the "AI-driven full-stack R & D methodology", we attempt to answer a question from the source: What exactly are the developers who truly know how to collaborate with AI doing right?

Not long ago, Bessemer (one of the most professional investment institutions in the US SaaS field) had a conversation with Cedric Ith, the founding designer of Perceptron AI. Perceptron is a cutting-edge company dedicated to building AI-native products, focusing on integrating generative models into the R & D toolchain to create a development platform with automatic optimization and self-learning capabilities.

In Cedric's view, AI is not a tool inserted at a certain node in the process, but should be embedded in the "semantic layer" of the entire design system, actively generating, judging and iterating at every stage of coding, interaction, debugging and deployment.

This article will share five key experiences summarized by Cedric, leading you to understand the real role that AI should play in the modern R & D process: from vibe coding to taste guidance, from natural language prototyping to full-stack delivery. This is a signal of the era about how humans and AI can jointly build products.

01 Taste is the moat, and design thinking is the new superpower

In an era when AI can write code in seconds, the difference no longer lies in "who can build", but in "who knows what to build".

Cedric once made a judgment: We are entering a world with extremely rich software resources and extremely low creation thresholds. This means that technology itself no longer constitutes a moat, and the real competitiveness has shifted to design thinking and product intuition.

When everyone can generate functional prototypes with natural language, those who can propose precise problem definitions, elegant solution paths and pleasant user experiences will gain an advantage. Execution speed, product perception and UI/UX details are becoming the new barriers in the AI era.

Meanwhile, AI is also redefining the design process. New generative algorithms allow designers to explore a large number of design concepts at an unprecedented speed. This is not just about "creating images faster", but enabling teams to automatically generate, evaluate and iterate on more human - centered solutions under user - defined parameters.

So, it is no longer a question of "whether you can write code", but a question of "whether you can ask good questions and quickly create a product that touches people's hearts".

The ultimate winners are not the teams with the strongest technology, but those who can most naturally combine AI's execution ability with human taste and judgment. They are the new generation of "product creators".

02 Natural language is a new design interface

In Cedric's workflow, we observed a very profound transformation: he no longer relies on traditional design tools, but uses natural language as the main design medium. He said: "The key skill is no longer writing code, but how to clearly and accurately express your ideas and changes so that AI can understand what you are saying."

This change is redefining the core capabilities of designers. Designers are shifting from "people who draw pictures" to "people who drive product structures with language".

The emerging key skill is called "design vocabulary" by Cedric - this does not refer to the ability to write code, but the ability to accurately describe modern frameworks, CSS attributes and interaction logic with language. For example, he uses terms like "4 - pixel rounded corners", "0.2 opacity" and "hover state" to interact with v0, and can generate a prototype with a bounding box interaction with coordinate tracking through prompts within a few minutes, which might have taken engineers several days in the past.

Behind this lies a new prompting ability: clear, consistent and shared language.

Clarity: Break down complex requests into simple and executable language. For example, instead of saying "add a label to the image", say "add a bold white text in the upper - left corner of each bounding box on the image to show the box number (e.g., Box 1, Box 2)".

Consistency: Once you name a function "segmented mode", always use this term consistently, rather than replacing it with other words in subsequent prompts.

Shared language: Just as traditional teams collaborate using standard terms, now you also need to "teach AI" your vocabulary. Cedric introduces key terms at the beginning of the design and uses them repeatedly, so that AI can "speak the same language as you".

Designers who are most adaptable to this change usually have two things in common: strong learning ability and the ability to switch tools. They can seamlessly switch between tools such as Figma, V0 and Cursor, constantly adapt to new interfaces, quickly master AI capabilities, and build product logic with "language" rather than "code".

As Cedric shows, future designers may not need to be engineers, but they must be system builders with high - resolution language skills.

03 The "design engineer" is on the rise

We are witnessing the rapid disappearance of the traditional dividing line between design and engineering. Cedric's workflow is a typical example: starting with Figma, completing an interactive prototype in V0, and finally making final adjustments in the codebase directly with Cursor.

This not only improves efficiency, but also redefines the way products are manufactured:

Closed - loop ownership is becoming the new standard

Designers are no longer just providers of visual solutions, but product promoters who can directly operate across the entire technology stack. As Cedric said: "I can directly contribute code and submit PRs to the codebase. This is a closed - loop system, and as a designer, I've never had such control before." The design intention no longer depends on engineers to "restore", but is delivered by designers throughout the entire process.

Static models are becoming obsolete

The past linear hand - over model - designers hand over images and engineers translate - is being replaced by a more collaborative way of working. Now, design delivery is no longer a static image, but a high - fidelity prototype containing interaction logic, or even a code framework with integration capabilities. Engineers no longer face a bunch of annotated images, but almost ready - to - go components that can be directly launched.

The iteration speed of design and development is greatly compressed

In the past, style modifications and function adjustments often took several days. Now, designers can directly handle them at the code level, without the need for rounds of screenshots, annotations and communication. The interval between design review and function implementation has been shortened from days to hours, promoting rapid product refinement and launch.

This paradigm shift is profoundly affecting team structures and recruitment logic. The most efficient teams usually have interdisciplinary capabilities - they can write code and understand products and user experiences. Those who can smoothly switch between design and engineering, build prototypes and promote implementation will stand out in the new AI - driven production paradigm. The future belongs to hybrid teams with both technical skills and good taste judgment.

04 Four principles of AI - native design

As the application of artificial intelligence accelerates, design principles for AI products are gradually taking shape. Cedric has summarized some new key points that are different from traditional software design, and they are being adopted by more and more excellent teams. Here are the four most notable principles:

Reduce cognitive load and let AI actively understand users

The best AI experience should be like "having a natural conversation with a smart person". This means that users don't need to click repeatedly, set parameters or think about the instruction structure, but can focus on expressing their intentions and let AI automatically handle context and details. For example, Recall AI and Granola well demonstrate this. They can automatically extract key information and insights from conversations without user presetting, truly achieving "effortless operation".

Accept non - determinism and gracefully handle "derailment"

Different from traditional software, the output of AI systems is often open - ended and multi - path, and there may be unstable or off - intention situations. Good design is not to avoid this characteristic, but to provide appropriate "supervision tracks". For example, OpenAI supports interruption and retry of long - process tasks through Temporal, and Cursor and V0 introduce "execution trees" and "rollback checkpoints", allowing users to quickly backtrack and switch paths when AI deviates from expectations, avoiding the frustration of "watching it go wrong helplessly".

Let AI show what it's "thinking"

Although the underlying model may be a black box, the interaction logic and reasoning process should be as transparent as possible. Perplexity shows an excellent citation mechanism, allowing users to know where the information comes from; Deepseek shows multi - step reasoning paths; Anthropic continues to advance in visualizing the "chain of thought". These practices not only enhance user trust, but also help them better calibrate AI output, thus achieving a combination of "controllability" and "interpretability".

Design for supervision, not operation

As AI becomes more and more capable of acting as an agent, users will shift from "executors" to "commanders". This requires design to focus on "coordinating multiple intelligent agents" rather than the traditional button - plus - operation flow. Early explorations have emerged, such as Perplexity's "background research notification", Codex's multi - thread progress bar prompt, and Comet's generative form interaction. These are all building a new interaction paradigm: users no longer drive step by step, but form a closed - loop of high - level instructions plus intelligent feedback.

These principles are still evolving, but one thing is certain: Teams that start building products around these dimensions today will be the first to create more natural and trustworthy AI experiences. Future AI products are not about making people "able to use", but about making people "want to use".

05 In the rising AI era, speed is everything

In an era when AI tools are changing rapidly, the speed of change is amazing. As Cedric said: "Maybe by the end of our discussion, v0 will no longer be the best tool." This is not a joke, but the most realistic portrayal of the current product environment. The rapidly changing ecosystem is forcing enterprises to shift from "building perfect products" to "building fast - learning organizations".

From our conversation with Cedric, we can extract several organizational characteristics that stand out in this environment:

Allow teams to actively try new tools, and do not use technical stability as the only evaluation criterion;

Give priority to "deliver first, then optimize", and prioritize the speed of learning and feedback;

Build a modular, API - driven architecture to enable the system to have rapid integration capabilities;

Emphasize that "learning speed" is as important as "professional experience", and encourage active adaptation to change.

For large enterprises, this transformation is especially important. Cedric's advice is practical: even if you can't directly influence the production system, you can quickly create high - fidelity interactive prototypes with AI tools to gain internal organizational recognition. Design is no longer just "design", but an experimental field for promoting organizational change.

This acceleration is compound - designers can create prototypes faster, engineers can implement functions faster, and teams can obtain user feedback faster. The entire product development cycle is compressed, and the density of innovation has never been higher.

Cedric's AI design stack is a microcosm of this trend:

Figma: It is still the "source of truth" for visual design, used for layout, structure and preliminary frameworks, but has limitations in handling dynamic interactions and state management.

v0/Lovable/Bolt.new: It takes over the output from Figma and supports defining dynamic behaviors with natural language. For example, you can draw a coordinate bounding box through conversation prompts and implement real - time interaction logic.

Cursor/Windsurf: It directly fine - tunes styles and interactions at the code level, such as "set the corner radius to 16px" and "number each box in sequence", and generates PRs to submit to the engineering team.

Component libraries such as Shadcn / Tailwind / UntitledUI/HeroUI: They provide standard component semantics for AI, greatly reducing the risk of hallucination. For example, users can directly say "use the toast component of Shadcn" or "apply Tailwind opacity of 20" to achieve consistent and controllable code generation.

This article is from the WeChat official account "Crow Intelligence Talk", author: Smart Crow, published by 36Kr with authorization.