王文锋

王文锋 frames OpenClaw as validation of a product form, not a fundamental paradigm shift. Manus used reasoning models to unlock deep research; OpenClaw pushes the coding capabilities of the latest models to their limit and makes the long-discussed ideas of proactivity and self-iteration actually work. 曲凯 therefore proposed that “all Agents will ultimately be coding agents.” 王文锋 agreed and went further: every Agent will eventually either wrap a coding agent or be built around one.

Vertical Agents and traditional SaaS both deserve deep skepticism, because terminal＋skill is converging different use cases on the same execution layer. In Anthropic’s disclosed Claude Code usage data, only 49.7% falls under software engineering; more than half is already office automation, data analysis, marketing, and copywriting. 王文锋 argues that SaaS once scaled expert capabilities through fixed UIs, while Agents directly understand objectives, generate solutions, and iterate—effectively giving every company a context-specific expert.

Coding Agents may already be capable enough; the real bottleneck is that most people do not know how powerful they are. Before the OpenClaw author became famous, he reportedly made about 1,600 code submissions in a single day. 王文锋 likened that to a year’s output from a 3- or 4-person team; Cursor also used an Agent to build a roughly 3M-line browser in a week. He rates top users at 90 and ordinary users at 10, stressing that the gap is not linear: “Someone who knows how to use it well could be 1,000x more efficient on their own.” Ordinary users may still be doing the wrong things or only simple programming tasks.

Moving from a million engineers to a billion ordinary users is the most direct product and distribution opportunity in this cycle. 曲凯 estimates that OpenClaw already has more than 2M Agents, though that may include multiple accounts deployed by one person. 王文锋 says Claude Code has reached 1M active users; based on the market share he cited, Codex could have 3–5x as many, putting the two at roughly 5M active users combined. Against his projected billion-user end state, penetration is “not even 1%”; the priority is not building smarter Agents, but flattening the barriers to configuration and management.

Progress on long-horizon tasks comes from combining model capability with filesystem engineering; true proactivity means letting Agents maintain their own context, identify needs, and manage other Agents. As tasks extend from dozens of steps to hundreds or even thousands, the filesystem makes errors observable and repairable: an Agent can inspect files such as memory and progress, detect deviations, and fix itself. 王文锋 ultimately envisions a meta Agent that identifies problems, configures specialized Agents, completes the task, distills the experience, and then releases them—“AI managing AI.”

AI may eliminate large numbers of execution roles, but humans will retain taste, tacit understanding, and final sign-off. 王文锋 believes the story of 10 customer-service representatives becoming 1 could spread to engineering teams, with 10 engineers reduced to 2. If a task must be explained in full, humans may already have lost to AI. Those who remain need enough long-term context to “know what someone is thinking with a single click” and take responsibility for the final merge and the “qualified” sign-off.

Sheet0 is productizing its internal Linear—Slack—GitHub automated development workflow for high-Token users. Once a request enters Linear, AI can build, test, attach screenshots, and submit a PR, turning engineers from Agent commanders into quality inspectors. 王文锋 says individual productivity is up at least 10x year over year; products that once took a year to ship now find “2 months too slow—2 weeks may be enough.” The team spent about $20k on AI coding last month, and its target market is defined not by individual versus enterprise, but by Token consumption.

This productivity revolution could also contract demand, which is why 王文锋 is only willing to solve problems for the next 3–6 months. If the “one-person unicorn” means removing large numbers of employees, wages, consumption, and upstream corporate demand could create a negative feedback loop rather than an endlessly expanding positive one. His startup strategy has shifted from predicting the end state 5–10 years out to “forecasting as a secondary input, following as the primary one”: “Don’t try to solve bottlenecks people have not encountered yet.”

🔗 Original source & video: After OpenClaw, I Only Want to Focus on the Next 3–6 Months | A Conversation with Sheet0 Founder 王文锋

The substance of this Agent boom is that products have finally crossed from the AutoGPT-style “toy” of April 2023 to delivering results in real work. 王文锋 breaks the progress into two tracks: o1 brought long-horizon reasoning, while the more easily overlooked shift is that Agent engineering has learned how to build Context, receive feedback from the environment, and call Tools; ultimately, results depend on “Context plus LM,” not simply on who has plugged into the strongest model.

MCP’s value is not creating a new capability, but standardizing the interface through which models call Tools into a universal unit of measure. 王文锋 compares Function Calling to version 0.1 and MCP to version 0.2; as long as a SaaS product has an OpenAPI, developers can wrap it with MCP themselves without waiting for native vendor support. He says A2A looks more like “a KPI exercise,” because an Agent itself can be wrapped as a Tool—a reflection primarily of the fight over who gets to set the standard.

API and GUI are not mutually exclusive generational paths; Agents should mix the two execution methods by use case. APIs are more stable, while GUI screenshot-based approaches are faster and consume “at least an order of magnitude” fewer Tokens, at the cost of possible coordinate-recognition errors; Manus’s decision to show the browser process also has product value, letting users see how it works and giving them confidence. 曲凯 counters that users ultimately care only about the result, and process transparency mainly backfills trust while Agents are still “not ready and unreliable.”

王文锋 believes we are now—and will remain for a long time—in the era of vertical Agents, because professional results require definable states, action spaces, and reward signals. His analogy is cooking: an ordinary person can follow a recipe, but that does not make them a five-star hotel chef; even if a general-purpose task has a 90% success rate at every step, multiplying across many steps quickly degrades the result, while vertical products use domain Know-how, long System Prompts, and structured validation to narrow and converge the path.

AI Coding is more like an Agent’s “dexterous hand”; reuse should be the default path. The optimal sequence is to first look for a Tool that has already been tested, and only write code on the fly if none exists. On preserving successful code to avoid the cumulative failure implied by (0.9^{10}), 曲凯 proposed the mechanism and 王文锋 said it was reasonable. 王文锋 expects Workflow and Agent to coexist for the long term: the former is human-driven, stable, and non-generalizing; the latter is AI-driven and generalizable, but may “mess things up five times out of ten.” His rough split in one passage is that AI may handle 20% of more open-ended problems, with the remaining 80% being more common problems; elsewhere he summarizes AI’s role as mainly handling routine problems.

The ideal interaction for an Agent product is not teaching users to write Prompts, but letting them complete tasks “like an extremely happy little child.” 王文锋 puts interaction flexibility ahead of initial accuracy, insisting that the chat box remains the most important entry point, with Human-in-the-Loop, preference memory, follow-up questions, and contextually surfaced components filling in the precision gap; simply opening Meituan or DiDi already supplies substantial intent Context, while truly high-quality data is not a single input-output pair but a complete sequence containing intermediate state changes.

SHEET0 is betting not on generating another report, but on turning heterogeneous data into a complete table and then driving action. Examples include identifying a GitHub user’s company from commit records and social accounts, and fully scraping companies from YC batches, finding their founders and Twitter accounts, then continuing outreach; 王文锋 claims the product can deliver “100% accuracy, 100% stability, and 100% no data loss,” but the episode provides no sample size or external validation. For investors, the more important question is not whether the team has an impressive algorithm pedigree, but “how are the incentive signals from the environment designed?”

王文锋’s industry timeline is constrained simultaneously by model leaps, inference costs, and the cycle time required for Context engineering. At the time of recording, he judged that GPT-5 might arrive “around the end of this year,” while reducing costs to one-fifth or even one-tenth of current levels could take another year, putting truly large-scale Agent adoption in the second half of 2026. On the Context side, excellent teams need at least 6 months and possibly a year, with a batch of better products potentially appearing in Q3 of that year. “The engineering complexity inside Agents is far beyond what people imagine.”

🔗 Original source & video: The First Half of Agent Development: How Environments, Tools, and Context Determine Agents | A Conversation with Sheet0 Founder 王文锋