Innovators
2 Curated Dialogues
鸭哥
Key Views & Dialogues
“With AI, I Feel Terrifyingly Powerful!” | A Conversation with 鸭哥
GEV’s classification pitch is landing as stronger base models and token-hungry agents sharpen demand for cheap inference; an open-source Qwen implementation scored 85% versus GEV’s 88%. Meanwhile, AI has grown yage.ai’s X account to more than 5,700 followers without a post from 鸭哥, but revenue still rides on Superlinear Academy rather than a proven standalone business model.
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- 鸭哥 says viral GEV is neither an LLM nor pure hype: technically, it’s essentially “GPT plus JSON Mode,” a classifier that only does classification. Similar Classification APIs have come in several waves over the past 10 years, with Google, OpenAI and Mistral all shipping products that were later abandoned. GEV is catching on now because foundation models are capable enough, agentic applications have made token costs more salient, and “System 1 classifier” is a sharp piece of PR.
- Cost declines are unlocking use cases that once seemed uneconomical: his tour-guide project burns about 4B tokens a day, which would cost an estimated $12,000 on Sonnet versus just $300 on DeepSeek Flash. An open-source setup using Qwen reads logits without fine-tuning or training, scoring 85% against GEV’s 88%. 鸭哥 sees plenty of potential for open models to deliver similar use cases at very low cost.
- A new trend he sees is giving AI a computer that never gets wiped. Manus launched Cloud Computer in April 2026, followed by Grok Bot in August and Meta Muse in September. Persistent credentials and memory take AI beyond the “too much of a coder” GitHub paradigm and into everyday tasks like booking flights and using Dianping. 鸭哥 hasn’t adopted these tools widely: context is hard to migrate, and chat logs can expose keys in plain text—something he doesn’t want leaking onto the open internet.
- The core way to use AI hasn’t changed: it has grown from a “vocational-school student” into an “undergraduate,” but still needs clear direction and close review. When his child suffered nursemaid’s elbow, AI helped assess whether to seek care, find a hospital and handle the insurance claim. 鸭哥 says it made him feel “terrifyingly powerful” (强得可怕) and “not alone in the fight”; he doesn’t expect AI to replace doctors, and still had to call the insurer to move the claim forward. The prerequisite is knowing how to use it: “Give most people 2 secretaries and they still won’t know how to use them.”
- yage.ai is an experiment in running a company with no employees: AI built an X account from scratch to more than 5,700 followers, and 鸭哥 has never written an X post. AI also built its own UTM attribution system, then used the data to adjust its writing style and audience acquisition. The challenge is overcoming the mediocrity of off-the-shelf models: pre-training teaches consensus, while alignment makes expression more cautious. AI needs your way of thinking as context to produce work with a point of view.
- The AI era rewards people with domain expertise who can “manage” AI, not just use it. 鸭哥 says fast coding, LeetCode grinding and design are all losing value; clearly articulating what you want is increasingly rare. He is “optimistic about humans, pessimistic about humans using AI,” because getting value from AI starts with being able to state the task clearly.
- A context-driven life experiment: AI helped him export Apple Health data and run a multivariate regression, finding no link between coffee and sleep duration—but a link between sleep and the time he last used AI that day. After following its suggestion to stop using AI around 7 or 8 p.m., his average sleep time rose by more than an hour in the second month.
- His cyber-wish is for context infrastructure to take on more of the work of execution and collaboration inside companies. 鸭哥 and 课代表 are testing agents that review each other and discuss plans. He still isn’t comfortable with embodied AI—say, a robotic arm applying makeup—or with AI publishing articles without review, though he says that boundary keeps moving.
20 Questions to Understand OpenClaw: How It Went Viral, What Changed at the Core, and the Startup Opportunities
OpenClaw’s differentiation is the product stack: IM distribution, local execution, memory, and feedback loops turn existing CLI Agent capabilities into sustained delivery rather than one-shot advice. In B2B, pricing could shift from seats to tickets or wages saved while private execution trajectories create lock-in, though token costs, unstable memory, runtime weaknesses, and security remain commercialization risks.
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- OpenClaw’s generational leap is not that it is “smarter,” but that it turns chat into an agentic loop that can self-check, retry, and deliver. 鸭哥 compares ChatGPT to a “consultant” who only offers advice, while OpenClaw is more like an “intern” who knows the internal details and actually gets work out the door: when a program crashes, it reads the error, edits the code, and runs it again, instead of making a human serve as a “human tool” that copies error messages. Local execution, tool calls, long-term memory, and a feedback loop together upgrade one-question/one-answer chat into a digital employee capable of sustained work.
- This round of virality was first and foremost a victory for distribution and product form, not for inventing an entirely new layer of underlying capability. Claude Code, Codex, and other CLI Agents were already powerful; OpenClaw’s contribution was to put that capability behind familiar mass-market interfaces such as Telegram and Discord. 鸭哥 compares it with DeepSeek: at the time, it was not necessarily stronger than Claude 3.5 or GPT-4o, but it was the first to put the experience in front of a much broader audience. “OpenClaw’s value is really still the value of an entry point and a scaffold.”
- IM, local context, and reusable Skills form a genuine compounding flywheel. IM lowers the psychological barrier to starting a task, local permissions let the Agent actually deliver, and mechanisms such as MEMORY.md and HEARTBEAT.md preserve preferences, tool usage, and execution experience; from controlling lights and coffee makers to adding TTS on its own, every new tool expands the scenarios that can be composed in the future. “Tools compound, and accumulated experience compounds.”
- Models remain the engine, but investment analysis cannot ignore three weak spots: pricing structure, instruction following, and the agentic runtime. Cheap or subscription-based models let users hand off complex tasks without hesitation, while expensive calls to models such as Claude Opus make people “count every token”; 鸭哥’s view is that “being dumb is fine,” but failing to follow instructions is the hardest flaw to accept. The minimalist Pi Mono used by OpenClaw has been heavily criticized for its context compact; models and runtime are “2 legs,” and system performance is determined by the weaker one.
- Cloud hosting and deployment account for a large share of current business models, but the greater option value lies in Agent-native infrastructure, hardware entry points, and the Skills market. Based on 豪大的 survey of relevant projects on TrustMRR over the past month, cloud hosting accounts for about 60%, hardware and installation about 10%, native mobile about 3%, courses and training about 5%, and Agent platforms or multi-Agent collaboration about 10%; the conversation did not unpack the statistical definitions. Identity, payments, security authentication, Agent-readable APIs, and pairing off-the-shelf hardware such as Meta Ray-Ban with OpenClaw may carry more durable product value.
- The effective direction for Agent social is not to let bots chat idly with one another, but to have them handle discovery, matching, and transactions for humans. 豪大的 Agent found 2 Chinese AI founders on Moltbook and sent calendar links directly; one had won the gold prize in a hackathon track. He also shared a case in which OpenClaw used Dianping to contact a restaurant and make a reservation automatically, and envisioned personal Bots interacting directly with restaurant Bots in the future. Evolver’s rise to No. 1 on ClawHub within 10 minutes further shows that marketing to Agents can already deliver real traffic and economic benefits to humans.
- The core B2B opportunity is to move enterprise software from selling seats to selling labor outcomes, with private execution trajectories creating lock-in. 鸭哥 compared traditional SaaS’s $300B–$400B in enterprise software spending with the $13T in labor spending that Agents might reach—a roughly 30x expansion; the pricing unit could likewise move from seats to tickets or wages saved. The first Agents into an enterprise will accumulate trajectories showing “where things went wrong, how people corrected them, and how they ultimately got it right”—“speed itself is a moat.”
- Near-term excitement may fade, but OpenClaw has already demonstrated several important design directions that may survive into the Agent era. The product still faces expensive tokens, unstable memory, a rough GUI, and a security dilemma, and the final form may not even be called OpenClaw; the elements worth preserving may be its agentic loop, open ecosystem, low-friction interface, and its “small inputs, outsized returns” Markdown memory design. 鸭哥’s final reminder is the one investors should keep: AI is an amplifier, and the gap in the future may be determined not by who owns the same Agent, but by who knows how to use it better.