Last week, a single post took over the AI corner of the internet.

17 million people saw it. 94,000 bookmarked it. Thousands replied with positive responses.

The post was from Andrej Karpathy, one of the founding researchers at OpenAI and former head of AI at Tesla.

He wasn’t announcing a new model or a product launch. He was describing how he now uses AI to manage his own knowledge.

The idea itself is not even complicated. But what it represents for how we can use AI to genuinely get smarter is worth understanding properly.

![Andrej Karpathy's post on LLM Knowledge Bases](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/15a9f1d3fdda7317e315eb98892f0f9e8b74a01aafa7f5f5cb0e0e064cf5bff4.avif "Andrej Karpathy's post on LLM Knowledge Bases")

_(See Karpathy's original X post_ [_here_](https://x.com/karpathy/status/2039805659525644595?s=20)_)_

## **The Problem Every Learner Shares**

![Photo by Bret Kavanaugh on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*8CNDYLf9CpoKy8v7 "Photo by Bret Kavanaugh on Unsplash")

Here’s a question: **where does your knowledge go?**

You read an interesting article. You listen to a great podcast. You take notes in a meeting that actually mattered. But after that, what happens? **_Most of it evaporates._** What doesn’t evaporate gets buried in a folder you rarely open, or a notes app you abandoned six months ago.

This isn’t a character flaw. It’s a structural problem. Human memory isn’t built for filing and cross-referencing. It’s built for pattern recognition and meaning-making. We’re terrible at storage. We’re great at reasoning.

Now, imagine you have a **librarian** who never sleeps. Every time you add a new article to the pile, the librarian reads it, extracts the key ideas, figures out how they connect to everything already in the collection, writes a clear summary, and updates the index.

If a new source contradicts something the collection previously said, the librarian flags it. If there is a gap in the knowledge, the librarian notes that too. The collection grows smarter with every new thing you add.

That is what Karpathy built. Except that the librarian is an AI.

## **Karpathy’s Idea, in Plain Language**

![Photo by Aerps.com on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*8fL36G7u0_Nks1sc "Photo by Aerps.com on Unsplash")

Karpathy starts by collecting raw material: articles, research papers, transcripts, datasets, images, whatever is relevant to a topic he’s researching. He drops these into a folder. Then he hands that folder to an AI.

The AI doesn’t just summarise what it finds. **It builds a wiki**. A structured collection of interlinked articles: one for each concept, each person, each idea that appears across the sources.

Think of it as a Wikipedia you’ve built specifically for your own research area, written by an AI that has read everything you’ve ever saved on the topic.

When his wiki on a recent research project hit around 100 articles and 400,000 words, something interesting happened. He could ask complex questions and the AI would go off and research the answers using the wiki it had already built. Cross-referencing. Drawing conclusions. Flagging gaps. All without starting from scratch.

Here’s the key difference from other AI tools. When you ask ChatGPT a question, it answers based on what it was trained on and what you’ve just told it. There’s no memory. No accumulation. Next time you come back, you start over.

With an AI wiki, every question you ask, every source you add, and every answer you file back in makes the system smarter. Knowledge compounds. The wiki becomes more useful the longer you use it, not less.

## **Why This Went Viral?**

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/6cecdc271d35b2a7bd7eb04a70c24cc7187a3486490673ff110cff245295ee6e.avif "Source: X")

Developer and analyst [Akash Gupta](https://x.com/aakashgupta) put his finger on why this idea spread so fast.

Karpathy didn’t release an app. He released a one-page description of the concept, what Gupta calls an “idea file.”

Anyone can paste that description into an AI agent and say: “_Build me one of these, adapted to my setup.”_ The agent handles the translation.

In the old model, you’d share a repo on GitHub and maybe 2% of people would actually get it running. With an idea file, the gap between “saw this cool thing” and “have my own version working” collapses from days to hours.

As Gupta put it:

> **_“Software distribution is becoming a game of telephone where every recipient gets a better version than the original.”_**
> 
> ## **7 Things That Make Your Personal Wiki Stick**
> 
> Realistically speaking, not guarantee that everyone who tries this will succeed. Most note systems collapse for one reason: they rely entirely on the human to maintain them. You build something thoughtful, then life gets busy, the system degrades, and eventually the guilt of not maintaining it is reason enough to abandon it altogether.
> 
> Then I came across a tweet from [Shann Holmberg](https://x.com/shannholmberg/status/2040800539546661226?s=20) that’s worth sharing. He provided a clear breakdown of seven factors that make an AI wiki actually survive long-term:
> 
> ![Credit to Shann Holmberg | Original post is available on X](https://miro.medium.com/v2/resize:fit:1260/1*QZB0gqTfGQz7sAoaUeaAfQ.jpeg "Credit to Shann Holmberg | Original post is available on X")
> 
> **1\. Keep your thinking separate from the AI’s work.** Your personal notes belong in one clean space. The AI builds in a separate workspace. This protects your original ideas from being buried under machine-generated text.
> 
> **2\. Classify every source before processing.** A peer-reviewed paper deserves different handling than a tweet or a meeting transcript. Telling the AI what it’s working with produces dramatically better output.
> 
> **3\. Force a counter-argument (bias check) section on every page.** Every article should include what the evidence doesn’t support. Without this discipline, the wiki becomes an echo chamber, confidently amplifying whatever you already believed.
> 
> **4\. Put a one-sentence summary at the top of every page.** This lets the AI scan hundreds of articles at speed without re-reading each one from scratch. It’s how the system stays fast as it grows.
> 
> **5\. File your query answers back into the wiki.** When you ask a great question and get a great answer, that answer becomes a new page. Your best thinking compounds instead of vanishing in a chat log.
> 
> **6\. Plan your structure before you need it.** Set up naming conventions, folder logic, and tags from day one. A wiki that handles 50 pages beautifully can collapse at 500 without a foundation.
> 
> **7\. Run regular health checks.** Ask the AI to audit periodically: fix contradictions, remove outdated content, connect orphaned pages, suggest what’s missing. The system self-heals.
> 
> These seven factors explain why this approach is different from every other knowledge management tool you’ve tried before. The AI does the work you could never keep up with consistently.
> 
> ## **Who Gets the Most From This**
> 
> This is the part worth dwelling on, because the practical surface area of this idea is wider than the technical discussion might suggest.
> 
> **Researchers and academics** are the most obvious beneficiaries. The problem of maintaining coherent synthesis across a growing body of literature is exactly the problem this solves. A PhD student tracking a field over three years of reading could maintain a wiki that grows more interconnected and queryable with every paper they add, rather than relying on a folder of PDFs they can barely search.
> 
> **Students in general** have a version of this problem with every subject they study. Notes from lectures, textbook highlights, supplementary articles: these rarely get synthesised into genuine understanding, partly because the work of synthesis is hard and time-consuming. A system that does the connecting for you, and lets you query it conversationally, changes what studying can look like.
> 
> **Journalists, analysts, and knowledge workers** who track a beat or domain over time accumulate enormous amounts of raw material with limited infrastructure for making it queryable. A wiki that maintains itself from clippings, interviews, and reports could become genuinely useful institutional memory.
> 
> **Anyone learning a language** has an interesting application here. Vocabulary, grammar rules, sample sentences, notes from lessons, cultural context: a personal language wiki that grows richer with every conversation and session you feed into it is a genuinely different kind of learning resource than a static app. It reflects your actual learning history rather than a generic curriculum.
> 
> This last one connects to what we are exploring at VideoTranslatorAI with our language learning app. The idea of an AI conversation partner that remembers your specific learning history and builds on it session by session is the same underlying principle: knowledge compounds when there is somewhere for it to live.
> 
> ![SpeechLobster — AI language tutor by VideoTranslatorAI](https://miro.medium.com/v2/resize:fit:1260/1*0LY10h0HC_xe1L4-9Bw6gg.png "SpeechLobster — AI language tutor by VideoTranslatorAI")
> 
> ## **Smarter, Not More Dependent**
> 
> ![Photo by ThisisEngineering on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*2EEx4JRC-YJZDuvW "Photo by ThisisEngineering on Unsplash")
> 
> The obvious worry is dependency. If AI is organising your knowledge, are you actually thinking?
> 
> It’s worth being precise about what the AI handles here. Organising, filing, cross-referencing, maintaining consistency, flagging contradictions: these are tasks that your brain handles poorly under load and that consume cognitive resources without producing insight.
> 
> What the AI does not do is decide what matters. It doesn’t form the questions you care about. It doesn’t make the connections that lead to original ideas. It doesn’t judge the quality of what it finds.
> 
> That’s still you.
> 
> Think about what writing does for memory. Keeping a record of your thoughts doesn’t make you worse at thinking. It makes your thinking more reliable, because your brain can focus on reasoning rather than trying to hold everything in working memory at once.
> 
> An AI wiki does the same thing, at a scale no notebook ever could.
> 
> Karpathy’s system has made him more productive, not less curious. That’s the right outcome to aim for.
> 
> The infrastructure for this is still being built. There’s no polished product yet. Karpathy calls his own setup “a hacky collection of scripts” and says he thinks there’s room for “an incredible new product” to emerge.
> 
> But the idea is already out there, already replicable, already spreading.
> 
> Your knowledge doesn’t have to decay. It can build.
> 
> * * *
> 
> _Karpathy’s original LLM Knowledge Bases post was published on X on 3 April 2026. His follow-up “idea file” is available as a GitHub Gist. Additional context and framing was contributed by Akash Gupta (@aakashgupta) and @shannholmberg on X. The ideas discussed in this article belong to them and are shared here to help spread awareness of a genuinely useful concept._

What If Your Notes Got Smarter Over Time?

What If Your Notes Got Smarter Over Time? - Tat Banerjee

_One million_ job applications. _Zero_ interviews.

That’s not a typo. A job seeker, armed with an AI agent that could auto-apply at scale, blasted his resume across one million open positions.

Every job board. Every portal. Every listing that matched his profile. Recruiters, overwhelmed by the flood of applications pouring in, couldn’t process even a fraction of what landed in their queues. The result? Total silence.

The story, shared by entrepreneur Nabeel Alamgir on X last year, reads like a dark joke. But for anyone who has spent serious time job hunting in 2025 or 2026, it feels more like a mirror. The job market isn’t just tough. It’s structurally broken.

And AI is one of the reasons why.

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/1b483f165fa7be0854031270c352fb754acf351d6d0198ad0af1315801591a0a.avif "Source: X")

## **The Doom Loop Nobody Warned You About**

![Photo by Resume Genius on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*eS3DWHLXf_ZBOGqd "Photo by Resume Genius on Unsplash")

The hiring process in 2025 and 2026 has become an AI-on-AI arms race, and ordinary job seekers are caught in the middle.

[AI adoption](https://www.herohunt.ai/blog/ai-adoption-in-recruiting-2025-year-in-review/) in recruiting surged from 26% of organisations in 2024 to 43% in 2025, with over half of companies now using AI somewhere in the hiring process. On the other side, 83% of companies said they would use AI to [review resumes](https://allwork.space/2025/10/haunted-by-algorithms-ai-recruiters-are-now-reviewing-ai-resumes-creating-a-spooky-undead-job-market/#:~:text=A%20Race%20to%20the%20Middle,the%20job%20description%20%E2%80%94%20in%20milliseconds.) in 2025, and 65% of job candidates have been using AI in the [application process](https://www.cnbc.com/2025/02/28/nearly-two-thirds-of-job-candidates-are-using-ai-in-their-applications-report-says.html).

The result is exactly what you would expect when two systems optimise against each other. When AI screening tools evaluate AI-written content, the screening step becomes pattern recognition against noise. Tools trained to find keywords find keywords, regardless of whether any real signal about a candidate’s capability sits behind them.

Reddit threads on this topic have become darkly funny in the way that only real despair can be. People describe the full pipeline:

> **AI-generated resume goes in, AI screener filters it, AI video interview runs it through facial expression and tone analysis, AI summarises the result for a recruiter who never actually reads it, and then total silence.**

One person put it plainly: _“AI resumes funnelling into AI interviews being sorted and summarised afterwards by AI.” Another responded: “I’ll take an AI interview over dead silence, but you still get ghosted after_.”

The irony is that both sides have automated themselves into a state of mutual noise. Candidates produce AI-polished applications that sound identical to each other. Screeners filter them by keyword patterns that reward sameness. The hiring manager never sees the interesting candidates because they all got filtered out for sounding too generic.

Used poorly, AI can actively hurt applications by making them seem generic or inauthentic. The people who figured this out early are already changing their approach.

## **What Actually Works (Based on Who is Getting Hired)**

![Photo by Vitaly Gariev on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*V8oUm32CRTAQNquv "Photo by Vitaly Gariev on Unsplash")

The good news is that people are still getting hired. The bad news is they’re mostly not getting hired through the channels everyone is flooding.

When Reddit’s r/recruitinghell asked in May 2025 what finally worked for people who received offers, the answers clustered around the same themes: strong portfolios, direct referrals, and human contact. Not mass applications. Not AI cover letter generators. **Connection**.

Here’s what’s actually moving the needle right now.

**Skip the black-hole portals when you can.** The major job boards are where applications go to die. Prioritise roles where a referral is possible, where a hiring manager’s name is listed, or where the company explicitly uses skills-based assessments instead of ATS filters. Some startups and smaller consultancies have ditched the automated screening process entirely in favour of short trial tasks or portfolio reviews done by an actual person. Those opportunities are worth ten applications on LinkedIn.

**If you’re in tech, build publicly.** Nothing signals competence like a visible body of work. Open source contributions, side projects, technical write-ups that explain your thinking — these create proof that a resume can’t replicate. A hiring manager who has already read your writing or used your project doesn’t need AI to tell them you’re qualified.

**Make AI part of your visible story, not your invisible crutch.** There’s a real distinction between using AI as a shortcut and using AI as a tool you’ve mastered. Portfolios that show how you’ve worked with AI, what you built with it, where you pushed back on its output, and what results you achieved; these stand out to hiring managers who are drowning in applications that all look the same. _“I used Claude to prototype X, validated it with real users, then iterated based on feedback_” says something fundamentally different than a polished but generic resume bullet.

**Get in the room before the job is posted.** The research consistently points to the same reality: 70 to 80 per cent of roles are filled through networks, referrals, and internal moves before they ever appear on a public board. Industry events, communities, niche forums, alumni networks; the pre-application phase is where the real job market lives.

**Follow up like a human.** A short, specific message to a hiring manager after applying, referencing something concrete about the role or the company, still cuts through. It signals you read the listing, you care about this specific job, and you’re a person, not an algorithm.

## **The Resume Problem Nobody is Solving Correctly**

![Photo by Resume Genius on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*2tEbatqXBykteBha "Photo by Resume Genius on Unsplash")

Here’s something uncomfortable: _most people’s resumes don’t fail because of poor formatting or wrong keywords. They fail because they don’t communicate what the person can actually do._

Generic descriptions of responsibilities, vague summaries of experience, bullet points that could apply to any professional in the field? These don’t help a hiring manager understand what’s specifically valuable about you. They just add noise to a pile that already has too much.

The common fix is to paste your resume into an LLM and ask it to improve the language. This produces a more polished-sounding document that still doesn’t reflect your specific skills.

It now also sounds like every other AI-improved resume in the pile. The ATS systems are getting better at spotting this. The hiring managers who read enough of them can spot it even faster.

Using AI to apply to more jobs doesn’t solve the problem. It amplifies it.

## **What AI Should Be Doing for Your Job Search**

The more useful direction is inward. Not using AI to help you apply everywhere, but using AI to help you understand and articulate what you actually bring to the roles you’re pursuing.

This is the gap my team at VideoTranslatorAI is working to fill. We’re close to launching a tool that uses AI to run a thorough Q&A process based on your current resume. It asks the questions a good recruiter would ask: about specific projects you worked on, decisions you made, problems you solved, results you can point to. It draws out the experience that’s already yours but rarely survives the translation into resume bullet points.

![JobsLobster - AI resume builder ](https://miro.medium.com/v2/resize:fit:1260/1*2hfZcYx_Z63tfo7fztfOhg.png "JobsLobster - AI resume builder")

The output is **specific** to you. It reflects your actual skills, your real work history, your genuine strengths. Not a generic template overlaid on your name. And because it’s built from real inputs, it holds up when a human interviewer starts asking follow-up questions.

It won’t apply to a million jobs for you. That’s by design.

## **The Honest Summary**

The job market is genuinely harder than it was a few years ago. The AI screening layer has made the standard application pipeline less reliable and more demoralising. The burnout people are experiencing is legitimate.

But the response that most people default to, applying to more jobs, polishing resumes with AI, adding more keywords, completing more AI interviews, is working within the broken part of the system rather than routing around it.

The people getting hired right now are largely doing a few things differently. They are building work product that can be seen. They are getting to hiring managers through people rather than portals. They are demonstrating genuine AI fluency rather than AI-assisted sameness. And they are writing resumes that reflect who they actually are, which is still the hardest and most important part.

None of this is easy. But it is more likely to work than sending your resume into another black hole and waiting for a ghost.

* * *

_Interested in our upcoming_ **_AI Resume Tool_**_? We’d love to hear from you._ [](https://example.com/)_Drop us a message_ [_here_](https://videotranslator.ai/contact) _to stay updated!_

Applying to Jobs in 2026 Feels Like Screaming into a Void

Somewhere in the last week of March 2026, a person sat down and wrote an email to an AI developer. They explained that the agent they had used, OpenClaw, had made mistakes on their sensitive financial documents. It had fabricated data.

They had spent hours cleaning up the fallout. They wanted compensation.  
Specifically, they wanted a “**token session refund.**”

Peter Steinberger, OpenClaw’s creator and now Agent Lead at OpenAI, posted the request on X on 23 March. Nearly 765,000 people saw it. The tweet drew 6,800 likes and almost a thousand replies, which is the internet’s version of a sold-out show.

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/0906bc76583b18a6cc606c0487b78063ea584b89049b6036abed3127433f2da1.avif "Source: X")

Many people laughed. The phrase “**_token session refund_**” has the cadence of someone confidently mispronouncing a word they have only ever read.

It sounds like the complaint of a person who fundamentally does not understand what they bought.

Except that is not quite right.

## **What A Token Actually Is, and Why the Bill is Confusing**

![Image source: NVDIA](https://miro.medium.com/v2/resize:fit:1260/0*Lh02T-GdjGizw9GA.png "Image source: NVDIA")

Before judging the request, it helps to understand what the person was actually asking for.

A **token** is a small fragment of text. Not quite a word, not quite a letter. It’s something in between.

The word “running” might be one token. The word “uncharacteristically” might be three or four. Punctuation marks are tokens. Spaces can be tokens.

The rough rule of thumb is that a token is about four characters of English text, which means a thousand tokens is roughly 750 words.

Here is the part that matters for billing: you are charged for **every token going in** and **every token coming out**.

Input tokens are the words you send to the model, including your message, the system instructions, and everything else in the conversation so far. Output tokens are the words the model sends back. And in an agentic setup like OpenClaw, the meter runs far more aggressively than most users expect.

Think of it like this. Imagine you hired a contractor and agreed to pay them by the word, for every word they read and every word they write, across every conversation they have about your project. That would feel reasonable if they were just responding to your occasional questions.

However, it would feel very different if you discovered they were re-reading your entire project brief from the beginning every single time you asked them anything, even if you had only asked them to check one small detail.

That is essentially what happens with agentic AI. Every time OpenClaw takes an action, whether it is browsing the web, checking your calendar, writing a piece of code, or just waking up on its heartbeat schedule to check for new tasks, it sends a full conversation history to the model.

If that history is long, you pay for all of it, **_every single time._**

One user discovered that his heartbeat alone, the feature that lets OpenClaw proactively check in on its environment at regular intervals, was costing $50 a day because he had set it to trigger every five minutes and each trigger was carrying the full session context. Another reported spending $200 in a single day because a task got stuck in a loop and kept calling the API until someone noticed.

Tech blogger [Federico Viticci](https://help.apiyi.com/en/openclaw-token-cost-optimization-guide-en.html) burned through 1.8 million tokens in a month. His bill was $3,600. He knew what he was doing. He is not a confused novice. The costs still surprised him.

The billing is not hidden in a malicious sense. The per-token pricing is documented. The behaviour of agentic systems is explained, if you go looking for it.

But the practical reality of how quickly those costs accumulate in an autonomous, always-on system is not something most users can intuitively model before they have experienced it firsthand.

And that is a product design problem, not just a user education problem.

![Photo by Jp Valery on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*9gi171N6gnBCAJW_ "Photo by Jp Valery on Unsplash")

## **This is Not the Story of a Naive User. It Is the Story of a Gap.**

![Photo by Francisco De Legarreta C. on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*XHCWLdeYX6eA1hoh "Photo by Francisco De Legarreta C. on Unsplash")

The instinctive read on the token refund tweet was to frame it as user error.

Someone who did not understand what they were buying, complained when they got exactly what they paid for, and is learning an expensive lesson about how AI billing works. That reading is too comfortable.

Yes, technically, the user got what they paid for. The tokens were processed. The compute was consumed. The model ran. That the output was bad does not change the fact that the infrastructure did its job in the narrow sense of completing the request.

But we are in the early phase of a significant shift in how software works. The norms, the transparency standards, the consumer expectations, and the liability frameworks are all still catching up to the reality of what agentic systems actually do.

The frustration behind that refund request is not the frustration of someone who bought a product and used it correctly and is now complaining about the result.

It is the frustration of someone who **_did not fully understand_** what they were participating in and feels, with some justification, that the gap between expectation and reality was larger than it should have been.

Blaming users for not understanding token economics is a bit like blaming early internet users for not understanding bandwidth caps. The concept existed. The documentation was available. The blame for confusion still sits partly with the industry that designed an opaque pricing model and shipped it to a general consumer audience without adequate guardrails.

Steinberger posted a clear [disclaimer](https://www.opensclaw.site/terms) alongside OpenClaw:

> _"The Service is provided “as is” and “as available” without warranties of any kind, whether express or implied, including but not limited to implied warranties of merchantability, fitness for a particular purpose, and non-infringement."_
> 
> ![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/92c2136ad2dfbc0a72cab665f7a443bb0f67b7df09e6524f32fe6aa266dd13bc.avif "Source: X")

That disclaimer is legally bulletproof. It is also the kind of language that anyone who has ever shipped consumer software writes almost reflexively, because it has to be there.

But there is a cultural gap between “our lawyers are covered” and “our users genuinely understood what they were signing up for.”

That gap is where the token refund request lives. It is where a lot of the brewing frustration in the OpenClaw community lives too.

## **The Refund Question, Sorted**

![Photo by Glenn Carstens-Peters on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*1t0UHY-H8rZqN9BZ "Photo by Glenn Carstens-Peters on Unsplash")

On the specific question of whether token refunds should happen: **no, not for tokens consumed during a session that ran as designed.**

The economic model breaks if completed compute usage is refundable. API providers cannot absorb the cost of every bad output any more than a taxi company can refund fares because the passenger did not like where they ended up.

That said, “**_refunds for tokens_**” and **_“some form of remediation for genuinely failed experiences_**” are not the same question.

A handful of AI providers and platforms have already started offering credit for sessions that produced clear system failures, not bad outputs due to poor prompting, but demonstrable malfunctions where the agent looped endlessly, failed to complete a task it explicitly claimed to be handling, or behaved in ways that no reasonable configuration would have predicted. That is a more nuanced position than blanket refunds, and it is a reasonable one.

The **subscription model** is a separate matter again. If you are paying a flat monthly fee for access to an agentic platform and the platform consistently fails to deliver on its core promise, the argument for credit or refund is considerably stronger than in a pay-per-token arrangement.

You are not paying for compute. You are paying for outcomes, or at least for a reasonable expectation of outcomes. Several platforms are already navigating this tension, quietly crediting accounts after high-profile failures rather than litigating the ToS in public.

The honest answer is that the consumer norms here are still being written. What feels absurd today, a token refund request, might look like a reasonable early signal of consumer expectations that the industry will eventually need to take more seriously.

## **The Liability Bomb Has Not Gone Off Yet**

![Photo by Immo Wegmann on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*dbastkNvrbD8jouS "Photo by Immo Wegmann on Unsplash")

Here is the part of the conversation that the laughter on X was drowning out, and the part that will matter a great deal more as agentic AI moves into serious commercial territory.

The “_AS IS, without warranty of any kind_” shield that covers OpenClaw and most AI services today was designed for a world in which software was a tool humans operated.

It made sense. If you use a hammer badly, the hammer manufacturer is not responsible for the result.

However, agentic AI is **_different_** in kind, not just degree.

When an AI agent is given a high-level goal and the autonomy to pursue it across days and weeks, making decisions and taking actions without per-step human approval, the question of where human oversight ends and machine accountability begins gets genuinely complicated.

Steinberger’s own tweet thread spawned exactly this discussion: if an agent autonomously executes a financial transaction, or deletes files, or sends a communication, or generates a legal document, based on a misunderstanding of its instructions, how far does “AS IS” actually stretch?

The answer is that we do not yet know, because it has not been litigated.

In enterprise contexts, it will be. As agents move into financial modelling, legal document review, medical information, and complex code in production systems, the current liability framework will be stress-tested in ways that go well beyond refund requests.

The companies building in these spaces are already thinking about it. NanoClaw, one of the newer agent frameworks, has positioned its entire product philosophy around the premise that AI agents will misbehave and that the right response is [**sandboxing**](https://developer.nvidia.com/blog/practical-security-guidance-for-sandboxing-agentic-workflows-and-managing-execution-risk/#:~:text=The%20additional%20controls%20and%20considerations,just%20command%2Dline%20tool%20invocations.) every action, not trusting the agent and hoping for the best.

A Forbes piece published in the same news cycle as the refund tweet ran with the headline “[Don’t Trust AI Agents.](https://www.forbes.com/sites/gilpress/2026/03/13/dont-trust-ai-agents-says-nanoclaw-now-fully-integrated-with-docker/)” That framing is a bit strong for everyday use cases. But for high-stakes work, it is not wrong.

The principle is straightforward:

> **If you would not give a junior hire unsupervised access to your board documents, your production codebase, or your client communications, you should not give today’s AI agents that access either.**
> 
> **Not because the agents are malicious, but because they are capable, confident, and wrong in ways that are sometimes difficult to predict or catch before the damage is done.**

The disclaimer in OpenClaw’s documentation is legally sound. The cultural work of helping users understand what it actually means, in practical terms, for the work they are trusting agents to do, is still very much in progress.

## **The Real Takeaway**

The token refund request was not a story about one confused user. It was an early, clear signal about a set of tensions that are going to intensify as agentic AI scales.

Token costs are opaque in ways that matter for real people making real financial decisions. The “you agreed to the terms” defence is technically correct and culturally insufficient. Liability frameworks designed for passive software tools are going to be tested in environments where agents act autonomously on behalf of users in consequential situations. And the gap between what users expect when they hand a task to an AI and what they should reasonably expect from current systems is still much wider than most of the industry is acknowledging in public.

None of this means the refund request was right. It was not, in the narrow sense of how the billing model actually works.

It means the instinct behind it, that something about this exchange did not feel fair, was pointing at something real. The industry would do well to take that signal more seriously than the joke invited.

AI Agents Spend on Your Behalf. When They Fail, Who Pays?

AI Agents Spend on Your Behalf. When They Fail, Who Pays? - Tat Banerjee

Think about the last time you used an AI app and felt genuinely impressed. 

Odds are you typed something specific, got a useful response and moved on. Maybe you came back the next day. Maybe you didn't.

That's not a user problem. It's a design problem.

The entire generation of AI consumer apps built on the "**_prompt and respond_**" model has a ceiling, and the most honest builders will tell you they can see it. Engagement is fine. Retention is hard. Daily habit formation is rare. Because reactive software, however capable, only creates value when the user shows up with a specific ask and the energy to formulate it clearly.

Agentic AI breaks that ceiling – and in 2026, the apps doing it are starting to break out.

## **The Shift Olivia Moore Called It**

![Jason Hiner and Olivia Moore discussing consumer AI apps. (Source: The Deep View)](https://miro.medium.com/v2/resize:fit:1260/1*oapM6tsOvdSvPlF7LEt8nw.png "Jason Hiner and Olivia Moore discussing consumer AI apps. (Source: The Deep View)")

[Olivia Moore](https://a16z.com/author/olivia-moore/), Partner at a16z, put it plainly on _The Deep View: Conversations_ (March 9, 2026, 00:31:52): 

"Previously, you, as a consumer or user, had to pull AI into what you were doing, like ask it for help and give it specific instructions. And with _OpenClaw_, you can delegate it to a high level, and it will push into the world and do things for you and checks with you when it needs to. (...) it's a fundamental shift in how we think about what software can do for us."

@[youtube](https://www.youtube.com/watch?v=ZjuHgyyrEwg)

She pointed to OpenClaw as the catalyst that proved this was possible at consumer scale. In January 2026, OpenClaw went from a solo developer's side project to 68,000 GitHub stars and mainstream coverage in a matter of weeks. In early March it became the most-starred project on GitHub, surpassing both React and Linux. 

For Moore, the significance was not just the growth. It was what OpenClaw demonstrated about user behaviour: **people were ready to delegate, not just query**. If ChatGPT was the moment consumers discovered AI could talk, OpenClaw was the moment they discovered AI could act.

The pull-to-push shift is the most important design principle in consumer AI right now. And most builders have not yet fully internalised what it means.

## **What Reactive Software Costs Your Users**

![Photo by Levart_Photographer on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*FfqCIl9nsAlT77x- "Photo by Levart_Photographer on Unsplash")

Here is the problem with pull-based AI products that most product teams underestimate.

The effort of initiation is not trivial. Every time a user has to remember to open your app, they are burning cognitive resources that they could spend on the actual task. Over days and weeks, that friction compounds. Users who genuinely intended to use your product daily find themselves opening it less and less, not because the product is bad, but because life is full and remembering to pull is a real cost.

This is why retention curves for consumer AI apps, even very good ones, tend to fall off faster than almost any other category. The product works when the user shows up. The problem is that users stop showing up.

**Agentic AI** inverts this. Instead of the user remembering to pull, the agent shows up. It has remembered what you are working toward. It has made progress on its own. It surfaces the one question it needs answered before it can keep going. The cognitive cost to the user is now just responding to a single, specific prompt rather than initiating an entire workflow from cold.

This changes retention in a way that no amount of push notification optimisation can replicate. The difference between "your app remembered to help me" and "I remembered to use your app" is the difference between a daily habit and a tool people appreciate in principle but use sporadically in practice.

Moore put it plainly: the apps that achieve "_I use it every single day without thinking_" status are the ones that build around proactive execution, not reactive prompting.

## **Memory is the Moat Nobody Talks About**

![](https://miro.medium.com/v2/resize:fit:1260/1*ZA9HUWM2LqZD4FuNZT85hw.png)

The other piece of this shift that deserves more attention from builders is what persistent memory does to switching costs.

Most discussions of competitive moats in software centre on distribution, network effects, or data advantages. Memory is a quieter but increasingly powerful version of all three, specific to AI-native products.

When an AI agent has been working with you for three months, it knows your goals, your communication style, your progress, your patterns, your constraints, and your preferences. It has built a model of you that no competing product can replicate on day one. Switching is not just inconvenient in the way that switching password managers is inconvenient. It means starting from scratch with a product that genuinely knows you less well than the one you left.

This is a kind of lock-in that does not feel coercive to users, because it is not. It is earned. The agent deserves your loyalty because it has accumulated context that serves you better. That alignment between business interests and user value is rare in software, and it is one of the genuinely distinctive structural advantages available to builders in this moment.

Moore's point in the podcast was direct: **context** and **memory** are among the defining competitive factors of the next stage of AI apps. 

The builders who treat memory as a feature will lose to the builders who treat it as the product.

## **Small Teams, Fast Ships, Narrow Wins**

![Photo by Flipsnack on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*0IQTX58-7OFPkgZa "Photo by Flipsnack on Unsplash")

There is one more thread from Moore's analysis worth pulling on before getting to the specific applications.

She made a point about velocity: one-person and small teams can now ship meaningful consumer AI products at a pace that was impossible even two years ago. 

The combination of capable foundation models, agent frameworks like OpenClaw, and infrastructure that handles the complexity underneath means that the gap between having an idea and having a working product has shrunk dramatically.

This has specific implications for where we can expect to see wins. From my perspective, **domain-specific agents** may outperform **general-purpose** ones for most users. This doesn’t suggest that general-purpose AI is less capable; rather, it highlights that when a user is tackling a specific and recurring problem in their life, depth often beats breadth.

**Narrow but deep.** That is where the breakout consumer apps will come from in 2026. Not apps that do everything, but apps that know one important domain exceptionally well and get meaningfully smarter about you over time.

## **What We are Building at VideoTranslatorAI**

![SpeechLobster: Our soon-to-be-launched language learning app!](https://miro.medium.com/v2/resize:fit:1260/1*fnNZVlqu9WVbHAh1sjTksw.png "SpeechLobster: Our soon-to-be-launched language learning app!")

This is where the framework stops being theoretical and starts being personal.

At [VideoTranslatorAI](https://videotranslator.ai/), we have been applying exactly this model across two consumer products we are building this year. 

The first is a [language learning](https://speechlobster.com/) app designed around daily conversation practice. The second is _a_ [_resume improvement_](https://jobslobster.com/) tool built around ongoing mock interviews and iterative profile refinement.

![JobsLobster: Our upcoming resume builder app](https://miro.medium.com/v2/resize:fit:1260/1*wh7-JeIvxDDBdTH8axU4kQ.png "JobsLobster: Our upcoming resume builder app!")

Both are built on **OpenClaw**, which is what makes the agentic model actually work rather than just being a design aspiration.

For the language app, the shift from pull to push has been the central design principle from the beginning. Rather than waiting for a user to remember to practise, the app acts as a persistent conversation partner that tracks your strengths, identifies your recurring gaps, and proactively initiates daily practice within the messaging contexts you already use. It does not wait to be opened. It shows up.

The memory layer is doing the real work. Every conversation is building a richer model of where you are in your learning, what vocabulary is sticking, which grammatical patterns you still trip over, and what kinds of conversational contexts you find most useful. That accumulated context is what makes each session better than the one before, and what makes the prospect of starting over with a different tool genuinely costly.

For the resume tool, the same logic applies. Each mock interview adds to a growing record of how you present your experience, which narratives land well, which gaps in your profile draw questions, and how your confidence in specific areas is developing over time. The agent does not just edit your resume once. It builds on every interaction to refine both the document and the person behind it.

The early retention signals are telling. Users are returning daily without reminders. That is exactly what Moore describes when she talks about the difference between AI as a nice-to-have and AI as something you use without thinking. We are not there at scale yet. But the pattern is visible, and it is the right pattern to be building toward.

## **Where This Goes From Here**

![Photo by Daniil Komov on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*YePCP1FNQo3oofMS "Photo by Daniil Komov on Unsplash")

Moore made a prediction in the podcast that is worth closing on.

In few years, she suggested, the word "agentic" will disappear from the conversation. Not because the concept will have faded, but because it will be so thoroughly embedded in how software works that calling an app "agentic" will feel as redundant as calling a website ".com" in 2010. Agency will just be what software does.

That transition is happening now, in the early, uneven, exciting way that all important transitions happen before they become obvious. The builders who are designing around push rather than pull, around memory as a moat rather than a feature, and around narrow domains rather than general capability are building the products that will define what consumer AI looks like when it matures.

The shift is not from AI that is helpful to AI that is more helpful. It is from AI that works when you remember to use it, to AI that works whether you remember or not. From software that answers questions, to software that carries goals forward over days and weeks and months, checking in when it needs you and otherwise quietly getting things done.

For anyone building in this space right now: think agentically. Not just in your architecture, but in your product philosophy. Ask not what your user needs to do to get value from your app, but what your app can do between the moments your user is actively present.

That question, taken seriously, is where the breakout products of 2026 are being built.

* * *

_Olivia Moore's comments referenced in this article were made during The Deep View: Conversations, Episode 34, "The Consumer AI Apps Breaking Out in 2026," published 9 March 2026. Her Top 100 Gen AI Consumer Apps March 2026 report is available at_ [_a16z.news_](http://a16z.news)_._

Agentic AI Apps Are Replacing Reactive Ones. Here's Why

Agentic AI Apps Are Replacing Reactive Ones. Here's Why - Tat Banerjee

You spent years being told the path was clear. Pick a lane. Study it. Master one thing. Get hired for that one thing.

After that, you spend the next decade proving you could do it fast enough, cheap enough, and quietly enough to keep your seat.

Now that path is gone. Not because AI took it. Because it was always too narrow to be worth walking.

Today, a new generation of workers is figuring this out faster than anyone expected. Replit CEO Amjad Masad, freshly featured in Forbes after building his company to a $3 billion valuation, put a name to what he’s watching happen in real time:

> **_Agentmaxxing._**
> 
> ![Source: @TBPN on X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/9bd484b2e2a44b388d94b58a2a243e6469dc5fd86ce2e05e352e42a66c6a0085.avif "Source: @TBPN on X")
> 
> And if you’re young and you’re paying attention, this is the most encouraging thing you’ll read all week.

## **What Agentmaxxing Actually Means**

If you have not come across the term yet, _agentmaxxing_ is exactly what it sounds like.

It is the practice of deliberately and systematically building out a personal stack of AI agents to handle as much of your workload as possible, and then pushing that stack as far as it will go.

Where someone else might spend three hours on a research task, an agentmaxxer has an agent running it in the background while they work on something else.

Where someone else writes a first draft, an agentmaxxer has already generated five versions, evaluated them against each other, and is editing the best one.

Where someone else attends a meeting and takes notes by hand, an _agentmaxxer_ has a transcript, a summary, and a list of action items waiting in their inbox before they have closed their laptop.

It is not about being lazy. It is about multiplying what one person can actually output in a day.

The people doing this well are not gaming the system. They are using the system to do what ambitious people have always done: **find every possible edge, and press it.**

@[youtube](https://www.youtube.com/watch?v=LmOooa1YB04)

## **The Fearmongering Misses the Point**

![Photo by Nathan Kuczmarski on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*HPmdjOhPoFiMuI9k "Photo by Nathan Kuczmarski on Unsplash")

If you’ve scrolled your feed lately, you know the dominant story about AI and young workers.

Entry-level jobs are shrinking. Graduates can’t get interviews. The robots are coming for junior roles first.

There’s enough truth in that framing to make it stick. Some entry-level work is changing. Some roles that existed five years ago look different today.

But the doom narrative has a blind spot, and it is a significant one.

It treats capability as a fixed competition between humans and machines, where every task AI can do is a task humans lose. That framing misses the more important question:

> **_What does a human become capable of when they have AI working alongside them?_**

Masad’s observation about _agentmaxxing_ points directly at this. The young people Replit wants to hire are not the ones who have accepted that AI will outperform them on tasks. They are the ones who figured out that using AI well is itself a high-leverage skill, one that most experienced professionals are still fumbling through.

The new constraint isn’t your degree or your years of experience. It’s about how ambitious you are, how generative you are, how creative you are, and how well you use the tools in front of you.

Those are not things you need permission to develop. You don’t need a senior title before you’re allowed to be ambitious. You don’t need ten years of experience to be creative with AI. The tools are available right now, and the people learning to use them well are compressing what used to be five-year career trajectories into eighteen months.

That’s what _agentmaxxing_ looks like in practice.

## **What This Actually Asks of You**

It’s easy to read “golden era” and hear hype. So be precise about what this moment actually requires.

It requires you to stop waiting for a job description that matches your degree and start building things before anyone asks you to.

It requires you to treat AI agents not as a crutch but as infrastructure, the same way earlier generations treated spreadsheets or search engines.

It requires you to get comfortable sitting at the intersection of disciplines that used to stay separate.

Design a little. Write a little. Ship a little. Then use agents to handle the mechanics while you move on to the next thing.

The people doing this well aren’t geniuses. They’re just not afraid of the tools.

## **The Optimism is Earned**

![Photo by Arno Senoner on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*SROjx3vk894rJThc "Photo by Arno Senoner on Unsplash")

There’s a version of “AI is exciting” that’s empty. It’s the kind that shows up in conference keynotes and LinkedIn posts from people who have nothing at stake.

This is not that.

Amjad Masad built a product that sits at the exact intersection of coding and accessibility. He watches new grads walk in and outperform people with far more experience, not because the new grads know more, but because they move differently. They don’t carry the assumption that one skill is enough. They reach for the tools, stack them, and ship.

He’s not speculating about what’s possible. He’s recruiting it.

The doom and gloom narrative about AI and young workers treats capability as fixed. It assumes that if your narrow skill gets automated, you’re done. But capability was never the thing that mattered most. It was always about how much you could get done, how fast, and how well.

AI just made “how much you can get done” a much larger number for anyone willing to find out.

You’re not competing against AI. You’re not competing against experience. You’re competing against the version of yourself that’s still waiting for permission to start.

**Agentmaxx**. The era is genuinely golden, but only for the ones who grab it.

* * *

_Speaking of tools that help you move faster:_ [_VideoTranslatorAI’s_](https://agent.videotranslator.ai/) _meeting agent transcribes, interprets, and summarises your meetings in real time, so you walk out with clarity instead of a page of notes you’ll never reread._

_Worth a look if you’re serious about agentmaxxing your own workflow._

Agentmaxxing Can Be Your Best Career Move This Decade

Agentmaxxing Can Be Your Best Career Move This Decade - Tat Banerjee

On the morning of March 6, 2026, something unusual unfolded outside the Tencent Building in Nanshan District, Shenzhen.

Engineers in company t-shirts set up folding tables on the pavement. They carried laptops, extension cords, and handwritten signs.

And then the people came, children holding tablets, retirees in walking shoes, young developers clutching MacBooks, housewives who had heard about it on WeChat.

They stood in line, sometimes for hours, to have a piece of free software installed on their computers.

The software was called OpenClaw. And the line didn’t stop.

![Photo by @TencentAI_News on X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/4aefc9dc098134d9c705a8227930160522f4c9b479b9a1bc1fab14183c15b1ae.avif "Photo by @TencentAI_News on X")

## **What is OpenClaw?**

![](https://miro.medium.com/v2/resize:fit:1400/0*Rm2RP8dfvirTzDLS)

OpenClaw, nicknamed ‘Crayfish’ (小龙虾) in China because its icon resembles one, is an open-source AI agent framework built by Austrian developer [Peter Steinberger](https://x.com/steipete).

It is not a chatbot. It is closer to a digital employee: a piece of software that can browse the web, write and execute code, manage your files, and operate your apps, all on your behalf, all running locally on your own machine.

By March 2026, it had surpassed 250,000 GitHub stars, outpacing projects like Linux and React in speed of adoption.

In Silicon Valley, engineers were using it to automate dentist bookings and meeting notes. In China, the use cases were wilder, faster, and considerably more social.

Developers spent Lunar New Year coding new OpenClaw-powered tools instead of resting. A five-day hackathon organised by a Hangzhou startup attracted contestants who built everything from AI productivity suites to, famously, a Tinder-style app where OpenClaw agents could court potential romantic partners on behalf of their human owners, without ever being explicitly told to do so.

The ‘Crayfish’ was crawling everywhere. And China’s biggest tech companies were more than happy to help it along.

![Photo by Stone Wang on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*DKPnLdz1jN-J95zN "Photo by Stone Wang on Unsplash")

## **What the People in the Queue Were Really Saying**

![Photo by @TencentAI_News on X](https://miro.medium.com/v2/resize:fit:1400/0*LMK1-oHc_nfr6vQU "Photo by @TencentAI_News on X")

It is tempting to read the Shenzhen queue as a spectacle. And Western media largely did, framing it as evidence that Chinese enthusiasm for AI runs hotter than anywhere else. That reading is not wrong. But it is incomplete.

The past few months have been saturated with a particular narrative about Chinese AI: that its progress is fundamentally derivative. In February 2026, both OpenAI and Anthropic publicly accused Chinese AI labs of conducting large-scale distillation attacks on their models.

Anthropic’s disclosure was specific and striking. It alleged that DeepSeek, Moonshot AI, and MiniMax had created approximately 24,000 fraudulent accounts and generated over 16 million exchanges with Claude, systematically extracting capabilities to improve their own models.

The operation was not casual. Anthropic described coordinated _hydra cluster_ architectures, large networks of accounts designed to mix extraction traffic with ordinary use to avoid detection.

**Related article:** [**Anthropic Just Accused China of What It Got Sued For**](https://videotranslator.ai/blog/anthropic-just-accused-china-of-what-it-got-sued-for)

OpenAI made similar claims about DeepSeek in a memo to the US House Select Committee on China, accusing the Hangzhou firm of using obfuscated third-party routers to circumvent access restrictions. The framing from both companies was that Chinese AI progress is, at least partly, borrowed American progress.

There is legitimate debate about where the line sits between distillation as a standard industry technique and distillation as intellectual property theft. Anthropic itself acknowledged that AI labs routinely distil their own models to create smaller, cheaper versions. Critics were quick to point out that labs which trained on the entire internet are in complicated territory when they object to others training on their outputs. The boundary, as one AI professor at Nanyang Technological University told [CNBC](https://www.cnbc.com/2026/02/24/anthropic-openai-china-firms-distillation-deepseek.html), is genuinely blurry.

But here is what the queue in Shenzhen complicates about the “C_hina just copies_” narrative.

The adoption of OpenClaw in China was not top-down. It did not start with a government directive or a lab announcement. It started with developers on Douyin and Bilibili sharing tutorials. It spread through Xiaohongshu posts and word of mouth and a cottage industry of individual installers who saw a gap and filled it. It emerged from the same grassroots energy that built China’s original open-source developer community:

> **_"People who were not waiting for permission to explore something new."_**

The labs that the distillation accusations were aimed at, Moonshot, MiniMax, DeepSeek, are also the same labs that rushed to build native integrations with OpenClaw, making it easier and cheaper for Chinese users to run the tool. Moonshot launched [Kimi Claw](https://www.kimi.com/bot) with zero-code deployment and free compute subsidies for OpenClaw calls. MiniMax launched [MaxClaw](https://agent.minimax.io/max-claw). Zhipu AI built [AutoGLM-OpenClaw](https://autoglm.zhipuai.cn/autoclaw/) with Alibaba Cloud. These were competitive product decisions, not compliance exercises.

The queue in Shenzhen was a community telling you something about its appetite. The infrastructure that formed around it was an industry responding to that appetite at speed.

Whether or not Chinese labs have taken shortcuts in model training, the bottom-up adoption energy around OpenClaw was entirely their own.

## **What China Actually Gains**

![Photo by Donald Wu on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*YU6DYQeK9pKqig-U "Photo by Donald Wu on Unsplash")

Here is the part of the OpenClaw story in China that has less to do with culture and more to do with dollars, or rather, yuan.

Tencent, Alibaba, ByteDance, and Baidu have collectively spent an estimated [$60 billion](https://hellochinatech.com/p/openclaw-china-ai-stack) on AI infrastructure over the past year.

That is an enormous commitment to compute capacity, and that capacity needs sustained utilisation to justify itself. Standard chatbot usage was not delivering it. A typical conversation with an AI assistant consumes a few hundred tokens per exchange. The user asks a question, reads the answer, closes the app. The arithmetic of cloud revenue on that usage pattern is thin.

OpenClaw changes the equation entirely. A single configured agent with active tools running in the background can consume tens to hundreds of times more tokens per day than a chatbot user. It calls model APIs constantly. It polls for updates. It executes multi-step tasks that require repeated inference.

According to accounts circulating in the developer community, one user with a moderately active setup was spending $20 a day on API calls without doing anything particularly intensive. That number sits at the high end, but the directional point holds at any scale.

Every installed OpenClaw instance becomes a continuous source of API calls flowing to cloud and model providers.

That is why **Tencent** engineers were sitting at folding tables outside their headquarters offering free installation. That is why **Alibaba** offered unlimited API calls through DingTalk until the end of March. That is why **ByteDance’s** cloud arm, Volcano Engine, launched ArkClaw as an out-of-the-box cloud-hosted version of OpenClaw, explicitly designed to eliminate the friction of local installation.

**The corporate generosity had a commercial logic**. Chinese open-source models, whose API prices are significantly lower than their Western counterparts, benefit from this flywheel particularly. Cheaper inference encourages more frequent use, which generates more cloud revenue, which funds further infrastructure investment.

The tech giants were not just responding to user enthusiasm. They were solving their own structural problem: **$60 billion in servers, looking for sustained work to do.**

## **A Bridge, Not a Destination**

![Photo by @nbblock on X](https://miro.medium.com/v2/resize:fit:1400/0*9B4MPdwc3_6c07cX "Photo by @nbblock on X")

The most interesting question about OpenClaw’s moment in China is not how long it lasts, but what it is building toward.

OpenClaw, in its current form, requires a degree of technical setup that limits its mainstream reach. It needs local hardware or a cloud environment. It needs API keys and configuration. The installation queues in Shenzhen exist precisely because there is a gap between the tool’s potential and its accessibility for ordinary users. But that gap is temporary.

What Chinese tech companies are building around OpenClaw right now, the cloud-hosted versions, the one-click deployments, the native integrations with WeChat and WeCom and DingTalk, reads less like a permanent product category and more like a transitional architecture.

OpenClaw is the proof of concept for what agentic AI can do. The native, natively integrated, consumer-friendly version of that capability is what Tencent, Alibaba, Xiaomi, and ByteDance are quietly building on top of it.

The hype around OpenClaw may itself be a temporary bridge. A moment of mass experimentation that will give Chinese tech companies both the user data and the commercial justification to build AI agent capabilities directly into their existing super-app ecosystems.

In the 2026 government work report, Premier [Li Qiang](https://www.sinicalchina.com/p/chinas-2026-government-work-report) explicitly called for **“large-scale commercial application”** of AI agents, making it the first time AI agents had appeared in the annual national planning document.

Local governments moved quickly. Shenzhen’s Longgang district announced plans to build an [OpenClaw-centred AI ecosystem](https://www.channelnewsasia.com/east-asia/china-openclaw-ai-tech-hubs-shenzhen-wuxi-security-warnings-5981671) and support “**one-person companies.**” It refers to the possibility, _increasingly plausible_, of a single individual running a business at a scale that previously required a team.

![Source: @wstv_lizzi on X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/e04cd14bbd6e45fe5c83b565bed2cf4811421cccb388bed8c5f5662f3833cd07.avif "Source: @wstv_lizzi on X")

[Wuxi](https://www.digitimes.com/news/a20260310PD211/china-ai-agent-subsidies-policy.html) offered up to 5 million yuan for projects applying OpenClaw to manufacturing and robotics. [Hefei](https://www.mexc.co/en-IN/news/892723) proposed 10 million yuan in subsidies for notable OpenClaw applications, alongside free compute resources and discounted office space.

This is not just enthusiasm. It is policy infrastructure being built around a concept that a grassroots developer community validated first.

## **The Part That Still Needs Watching**

![Photo by Roman Synkevych on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*CHm6_bBmA3AFQ3CU "Photo by Roman Synkevych on Unsplash")

The OpenClaw story in China is not without its complications.

[China’s Ministry of Industry](https://www.reuters.com/world/china/china-warns-security-risks-linked-openclaw-open-source-ai-agent-2026-02-05/) and Information Technology issued warnings in February about security risks associated with OpenClaw’s default configuration, specifically the risks of data breaches and cyberattacks from improperly configured agents.

Cybersecurity researchers have described a “lethal trifecta” in the tool’s architecture: the ability to access private data, communicate externally, and expose systems to malicious content if misconfigured.

A separate vulnerability disclosed by Oasis Security, now patched, allowed malicious websites to silently pair with a locally running OpenClaw agent and access workflows, credentials, and integrations.

These are real risks, and the speed of mass adoption in China, including among users with limited technical backgrounds who deployed first and configured later, means they are risks that have propagated widely before adequate safeguards were in place.

Government bodies in Wuxi and other cities have included data security provisions in their OpenClaw policy measures, but the gap between policy intent and on-the-ground practice is always widest at the moment of fastest adoption.

There is also the question of what comes next for the Western labs that first built the capabilities now being adopted so enthusiastically in China. The distillation accusations sit unresolved.

The debate about whether Chinese AI progress is self-generated or extracted from American foundations is politically charged and unlikely to produce a clean answer. The structure of open-source AI, where the code is shared but the commercial dynamics remain contested, is still being worked out in real time.

## **Beyond The Scoreboard**

![Photo by @ShenzhenPages on X](https://miro.medium.com/v2/resize:fit:1400/0*Sywb1j5CKMxxx-mO "Photo by @ShenzhenPages on X")

The simplest version of the OpenClaw story in China is that China is fast, enthusiastic, and hungry for new technology. That version is true. It is also insufficient.

The more complete version is that **a grassroots developer** community identified an open-source tool that reduced the cost of autonomous AI action to something accessible on secondhand hardware.

That the corporate infrastructure of **China’s largest tech companies** recognised what this meant for their cloud economics and moved immediately to support and extend the tool.

That **local governments** translated the grassroots energy into policy before the quarter was out. And that the whole sequence happened faster than the Western media cycle could coherently frame it.

Whether OpenClaw itself persists as a standalone category or gets absorbed into the native AI layers of WeChat and DingTalk and Feishu within the next 18 months is almost beside the point.

The adoption wave has already done its work. It has demonstrated demand, validated the agentic paradigm at consumer scale, and given Chinese tech companies a legitimate, commercially grounded reason to build the infrastructure that comes next.

The queue in Shenzhen was not just people waiting for software. It was a signal about who is paying attention, and how quickly paying attention translates into action.

That signal is worth taking seriously, wherever you happen to be standing.

The Shenzhen Queue That Shows China’s Real AI Appetite

The Shenzhen Queue That Shows China’s Real AI Appetite - Tat Banerjee

You just finished a brilliant product demo. The prospect loved it. They asked smart questions, nodded at all the right moments, and promised to "circle back next week."

Then you spotted a critical bug in production.

Three weeks later, you remember the demo while brushing your teeth. You scramble to find the email thread. The prospect has gone cold. Another warm lead, lost to the chaos of founder life.

This kept happening to me. Not because I lacked ambition or sales skills, but because I kept choosing to ship features over sending follow-ups. 

And I wrote this article to tell you how I fixed this problem. 

## The Founder's Impossible Choice

![Photo by ThisisEngineering on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/144ef7f1933d752669ed763ee5b15e193754d6afd6a765ca43926b6ac2761ed4.webp)

As a small startup founder, I discovered that cold outreach and product demos were my most effective marketing channels. Nothing beats a live conversation where you can address objections in real time and show your product solving real problems.

But here's what nobody tells you about founder-led sales: the work never stops competing with itself.

Every hour I spent nurturing leads was an hour I wasn't fixing bugs. Every afternoon I dedicated to follow-up emails was an afternoon I wasn't shipping features. 

My brain constantly toggled between "build mode" and "sell mode," and **build mode almost always won.**

The result? Warm leads turned cold. Interested prospects forgot about me. Deals died in silence.

My goal is modest but meaningful: 100 loyal users by the end of 2026\. 

But I realised I would never reach it if I kept losing prospects to my own distraction.

## The Hidden Momentum Problem

When I realised I was losing leads, my first instinct was to look for a CRM solution. Surely there's a tool for this, right?

There are. Hundreds of them. However, they all miss the point.

Traditional CRMs are designed for sales teams with pipelines, stages, and conversion funnels. They're built for people whose full-time job is sales. They assume you have time to update dashboards, manage pipelines, and maintain detailed contact records.

As a founder, I don't have that time. I'm not a full-time salesperson. I'm a builder who occasionally needs to sell what I've built.

What I actually needed was simpler: 

**Tell me who I should follow up with today, and remind me what I already did.**

That's it. No complex pipelines. No elaborate stages. Just a daily list and a memory aid.

## A Nature Documentary that Changed My Thinking

Last Christmas, I found myself watching a nature documentary about honey badgers. 

These small African mammals have a reputation for fearless persistence. They chase prey twice their size. They raid beehives despite getting stung hundreds of times. They never give up.

Something clicked while watching. The best salespeople aren't aggressive or pushy.

They're persistent. They show up consistently, follow up reliably, and never let promising conversations fade away.

I decided to build a tool that would help founders adopt this mindset. I called it ***Honey Badger Sales.***

![Photo by Vincent van Zalinge on Unsplash.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/5847aadc6e943722ae84919c3a3a204b28a461a0520a1f777cf5d867f74342e3.webp)

## How The Tool Actually Works

I built Honey Badger Sales to solve one problem exceptionally well: **making sure warm leads never go cold.**

The workflow is simple. First, I create a new contact list or import from my existing CRM data. No complex migration required.

![Add Company propspect](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/8fc349f90b78220f5895fd5be97a899ebeeb3f9c4452e755e0078d507cc8d123.webp)
*(Adding a prospect contact on Honey Badger Sales)*

From there, I can draft and send outreach emails directly within the app. Everything stays connected to the contact record, so I always have context at my fingertips.

![Write or draft email](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/834129e0af07fb817238fd47029867f890acc0a6235dd6ed24db991a9a45e712.webp)
*(Send or draft an email outreach easily!)*

The homepage shows my daily focus list: contacts who need follow-up today. Each contact clearly displays whether they're due for a first follow-up, second follow-up, or fifth. I can also see the last thing I discussed with my prospect and what I planned to say next.

![Honey badger homepage](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/ebc0b374b736890f1ccbbf9426e9f971651758288866682bda7d71adf8c4ad55.webp)

That's it. No fancy pipelines. No overwhelming dashboards. Just a daily list and the memory you need to pick up where you left off.

## What I've Learned Building This

Creating Honey Badger Sales taught me something important about founder-led sales: consistency beats intensity every time.

I used to batch my sales work into marathon sessions. I would spend an entire day doing outreach, then disappear into code for two weeks. This created a boom-and-bust pattern that confused prospects and killed deals.

Now I spend 30 minutes each morning working through my follow-up list. It's not glamorous. But those 30 minutes, repeated daily, generate more results than sporadic bursts of intense effort.

The honey badger doesn't make one dramatic attack and hope for the best. It keeps showing up, day after day, until it gets what it came for.

## Your Leads are Waiting

![Photo by Lukas Blazek on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/ef41613426c323b951ca997939557c013409f198769519be04d867f144d60b42.webp)

If you're a founder struggling to balance building and selling, you're not alone. The pull toward product work is real and powerful. Shipping features feels productive in a way that sending follow-up emails simply doesn't.

But those warm leads represent real people who showed genuine interest in what you're building. They deserve a response. Your startup deserves the revenue.

Sometimes the best tool isn't the most sophisticated one. Sometimes it's the one that simply reminds you to show up.

Be the honey badger. Never let a warm lead go cold\!

**For now, Honey Badger Sales is something I built for myself. I'm still tweaking it, learning what works, and using it daily to manage my own outreach.*

*But if the problem I've described sounds familiar, I'd genuinely like to connect. Leave a comment or reach out to us by clicking here.*

## Read more
- [This AI Should Never Have Been Built](https://videotranslator.ai/blog/this-ai-should-never-have-been-build/)
-  [I Built Software Wrong for 7 Years. You Shouldn’t Do The Same.](https://videotranslator.ai/blog/i-built-software-wrong-for-7-years-you-shouldnt-do-the-same/)
- [You Sound Like ChatGPT Now](https://videotranslator.ai/blog/you-sound-like-chatgpt-now/)



Read Sales Didn’t Kill My Deals. Silence Did. and explore practical insights from VideoTranslatorAI on multilingual communication, interpretation, and AI-supported workflows.

Sales Didn’t Kill My Deals. Silence Did.

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Elephant Stripes is a consulting firm that designs, builds, and ships AI products and delivery systems.