Something we've been working on for a while is finally ready to share. 

_Introducing_..

## **Elephant Stripes**

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/8bb1dffc0d2da508353376deb71447a01073028001f85619b168413ce83c338c.avif)

We've been quietly building something new. [Elephant Stripes](https://elephantstripes.ai/) is our bespoke app development consultancy, and it's now live.

At Elephant Stripes, we craft custom, enterprise-grade applications tailored to the exact needs of each client. Our AI-assisted development process lets us deliver 3x faster than traditional build timelines, without compromising on precision, flexibility, or quality. Every line of code is purpose-built. No templates, no off-the-shelf shortcuts.

It represents the next evolution of the work we've been doing, and we couldn't be more proud to share it with you.

## **Our Content is Moving Home**

As Elephant Stripes grows, so does our content strategy. Articles covering app development, AI, consulting, and the broader topics we write about will now live on the Elephant Stripes blog.

The VideoTranslatorAI blog isn't going anywhere. It will continue as the dedicated home for VideoTranslatorAI product news, updates, and announcements. That content stays focused and stays here.

For everything else, come find us at [](https://elephantstripes.ai/)[elephantstripes.ai](http://elephantstripes.ai).

## **To our VideoTranslatorAI Community**

We wouldn't be launching anything new without the readers and subscribers who have supported VideoTranslatorAI from the beginning. If you've been part of that journey, thank you. It's meant a lot to the team, and it still does.

We hope you'll carry that support over to Elephant Stripes. We've got a lot more to share.

The VideoTranslatorAI Team

An Important Update from The VideoTranslatorAI Team

Read the VideoTranslatorAI team update on the Elephant Stripes soft launch and what it means for future product and blog updates.

VideoTranslatorAI Team Update on Elephant Stripes

Read practical guides on multilingual communication, live translation, transcription, AI workflows, and VideoTranslatorAI updates.

Translation, AI Workflow, and Product Guides

Read practical guides and product updates on multilingual communication, live translation, transcription, AI workflows, and meeting notes.

VideoTranslatorAI Blog on Translation and AI Workflows

Blog

Last November, I wrote an article about AI and translation. A translator who works between Japanese and English recently left a comment on it that I have been thinking about ever since.

She wrote:

_“Everyone says with AI doing the translation work, it will allow translators to focus on higher order work, but no one tells how much translators are getting paid for such higher order work.”_

She was not wrong. And I want to address that directly, because articles like mine, including my own, have been repeating a comfortable narrative that is not the full truth.

The comfortable narrative goes like this: **AI is a tool, not a replacement.** **Translators will move up the value chain. The work becomes more interesting. Everyone wins.**

The uncomfortable reality is that translation rates have plummeted. And if you have trained for this work, built a career on it, and are good at it, that is genuinely awful.

No amount of enthusiasm about the potential of AI changes what is happening to people’s incomes right now.

## **What the Comment Was Really Saying**

![](https://miro.medium.com/v2/resize:fit:1400/0*U1dg9-YU8iGHU8UH.png)

She made a specific point that deserves to be taken seriously on its own terms.

She has been using computer-assisted translation tools like TRADOS for a decade. She is not anti-technology. Translators have never had a problem adopting tools that help them do better work faster.

The problem, as she put it, is that _“business and tech leaders are not presenting a complete true picture.”_ The articles, the conference talks, the LinkedIn posts. They celebrate productivity gains, new workflows, expanded reach. They do not talk about the income collapse happening at the same time.

She is right about that. And I think I owe it to her, and to every translator in the same position, to be more honest about it.

## **The Maths Nobody Wants to Say Plainly**

![Photo by Rodeo Project Management Software on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*6gT4IPD4TnSNF5_m "Photo by Rodeo Project Management Software on Unsplash")

Here is a simple way to think about what has happened.

If one translator using AI can now do the work that five translators did in 2019, the market changes. That is not a precise statistic. But even directionally, it points to something real. Four of those five translators either need to find different work or accept significantly lower rates as supply exceeds demand.

The total volume of translation might increase, because work that was previously too expensive to translate is now getting done. But the income per translator does not keep pace with productivity, because productivity gains flow to buyers and platforms, not to the people doing the work.

This is not unique to translation. It is happening across every knowledge-based profession that AI has touched. But translation has been hit particularly early and particularly hard because the core task of converting language from one form to another is exactly what large language models are structurally well-suited for.

The tools exist. The rates have moved. And the safety net, the institutional infrastructure that would help translators adapt and capture value in the new environment, does not yet exist in any meaningful form.

## **What the Future of Translation Work Probably Looks Like**

I want to be honest about something here. I do not have a complete answer to the question she raised. What I have is a direction that I think is probably right, and a clear acknowledgement of what is missing.

The direction is what I would call:

> **_Compute Allocation._**

Let me explain with an analogy from my own work. I write software. I used to write it by typing out code directly. Now I write detailed prompts that tell the AI what to build.

I have gone from being a programmer in the traditional sense to being a **compute allocator**: someone who decides what is worth building, how to direct the AI toward it, and where the AI’s output needs human correction.

My guess is something similar will happen with translation. The work shifts from manually translating every sentence to directing the AI, allocating attention to the parts that genuinely need human judgment, and deciding what is worth translating in the first place.

![Photo by Per Lööv on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*6COqe4hh_pVOjeIU "Photo by Per Lööv on Unsplash")

Practically, this might look like a translator who understands a particular language and culture well enough to spot that a YouTube channel has a real audience in their language that the creator does not know exists. They translate it, using AI for the volume of work. Their value is not the typing speed. It is the cultural intelligence that identifies the opportunity and the taste that knows which 5% of the output needs a human hand rather than an AI second draft.

That is real expertise. It is different from what translation work looked like in 2019, but it is no less skilled. In some ways, it requires more.

## **The Gap That Has Not Been Filled Yet**

Here is where I want to be direct about what is missing, because this is the part of the conversation that usually gets skipped.

The future I just described, where translators operate as compute allocators and cultural curators, only works if the economic infrastructure exists to pay them for that role. Right now, **it largely does not.**

![Photo by Jakob Owens on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*zMbgfFKSojLDHSg1 "Photo by Jakob Owens on Unsplash")

If a translator identifies an opportunity, translates a video channel into a new language market, and that translation drives genuine audience growth and revenue for the creator, how does the translator capture a share of that value?

Currently, the answer is: **they mostly do not**. They get paid a one-time fee, if they are lucky, and the upstream value they created disappears into someone else’s analytics dashboard.

What would need to exist is something like a translation rights and revenue-sharing model. A platform where creators make their content available for translation, translators take on the work knowing they will receive a share of the downstream traction, and both parties benefit as the audience grows. Think of it as translation curation with ongoing royalties rather than flat-fee work-for-hire.

Something like this does not exist in any mature form today. The pieces are technically possible. The economic model is coherent. The reason it does not exist yet is that it is a genuinely hard coordination problem: you need creators willing to share revenue, translators willing to take on speculative work, a platform able to track attribution across languages, and a payments infrastructure that can handle micro-distributions at scale.

That is not an excuse. It is an honest account of why the transition is painful right now, even for people who can see where it is heading.

## **What We Believe at VideoTranslatorAI**

Again, we need to say this clearly.

We did not build [VideoTranslatorAI](https://agent.videotranslator.ai/) to replace translators. We built it to make multilingual communication accessible in settings where it was previously impossible: a nurse trying to explain a diagnosis to a patient who speaks a different language, a small business owner trying to reach a customer overseas, a community health worker trying to run a group session across five different languages simultaneously.

These are **not situations where a professional translator was sitting ready and got displaced**. These are situations where **no translation was happening at all, because it was impractical or unaffordable**. The access gap we are trying to close is real, and the people we are trying to serve genuinely needed the tool.

Press enter or click to view image in full size

![](https://miro.medium.com/v2/resize:fit:1400/1*jYCkSEC60cFH6H6NgWOn4Q.gif)

At the same time, the world is changing in ways that affect professional translators, and pretending otherwise is not honest. The demand for high-volume, routine translation work is increasingly served by AI. The demand for judgment, cultural knowledge, and the kind of taste-based decision-making that determines what is worth translating for whom is not going away. It is, if anything, becoming more valuable.

The problem is that the gap between those two realities is where a lot of skilled, experienced translators are currently sitting, watching their rates fall without a clear path to the higher-value work they are told is waiting for them.

We acknowledge that. It is not a gap we created, but it is one that the industry, including companies like ours, has a responsibility to think about more seriously than it has been.

## **To the Translator Who Wrote That Comment**

Thank you for saying it plainly. Your comment was more useful than most of the optimistic takes I have read on this topic, including what I wrote.

The income collapse is real. The higher-order work is real in potential but not yet real in practice for most people. And the infrastructure that would make it real, the rights frameworks, the revenue sharing models, the platforms that let translators monetise cultural intelligence rather than volume, has not been built yet.

That is the honest picture. It does not mean translation as a profession is finished. It means the profession is in a painful, unresolved transition, and the people experiencing that transition deserve a clearer acknowledgement of what they are dealing with than they have been getting.

The tools will get better. The economic models will eventually catch up. Neither of those things helps someone whose rates have already dropped by half.

That part is genuinely hard, and I do not want to minimise it.

* * *

_The original comment referenced in this article was left by a Japanese to English translator on the following Medium Article:_

[_AI Translation is Already Running These 7 Jobs_](https://medium.com/@tathagat.banerjee/ai-translation-is-already-running-these-7-jobs-91e26fe86302?source=post_page-----2a61934bbb92---------------------------------------)

_Regular updates on AI, vibe coding, and more have moved to Elephant Stripes. Subscribe to the newsletter_ [_here_](https://elephantstripes.ai/newsletter)_._

A Translator Called Me Out. They Were Right.

The income collapse nobody in AI wants to talk about.

The income collapse nobody in AI wants to talk about. Last November, I wrote an article about AI and translation. A translator who works between Japanese and English recently left a comment on it...

Video calls should feel seamless, not stressful. The **How to Set Up a Video Call Tour** is our newest guided walkthrough, designed to help you configure VideoTranslatorAI for live video calls so that every participant, regardless of their language, can follow along clearly and in real time.

## **What Is the Video Call Tour?**

The Video Call Tour is an in-app guided experience that walks you through VideoTranslatorAI's built-in video call mode. 

In just a few steps, you can configure your call mode (interpret or transcribe), generate unique links for each participant, set their language, and run a live transcriber/live interpreter for your video call.  

## **Tour Overview**

### **Activating Video Call Mode**

From your active chat, select Video Call mode. The tour walks you through this single starting point and explains what happens next.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/c0e374177352487deed060cf70b5702d461bd62b7aeca88adbce0e88de359bc7.avif)

### **Opening the Setup Form**

This shows you how to open the setup form, where you can customise your meeting details. 

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/c3631ad1d4754222935494450bcc8a5edfa1603827887f3f0475926528909537.avif)

### **Filling In Meeting Details**

Here, set up meeting time and provide the participant's name and email address. If you choose interpret mode, this is where you assign a language to each participant based on their preferred language. 

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/58e76dc07111926c9c36c4fa9392e4d30967aff5143a53a225f62908d840a6cd.avif)

### **Sending Meeting Invitations** 

Discover how to generate meeting invitations and translate them into each participant’s language. 

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/e614ccfef671075ac9b8a9ee344491f9cdd8792c8986689494eee6b0d1b7e530.avif)

Once both parties are connected, the call runs with live subtitles displayed automatically. Optionally, enable voice-over so each participant hears the conversation in their chosen language in real time.

## **Goals of This Tour**

*   Help users start a video call in under two minutes
    
*   Clarify the moderator and participant link system so no one joins the wrong session
    
*   Eliminate setup confusion for first-time video call hosts
    

## **Who Is This Tour For?**

This tour is ideal for professionals hosting client calls, consultations, or interviews directly through VideoTranslatorAI, without needing to connect to any external meeting tool.

## **Try It Today**

The Interpret Tour is now available in the VideoTranslatorAI app. Access it from the onboarding flow or at any time via Help > Guided Tours.

Ready to jump into a video call with confidence? [Open the app and start the tour →](https://agent.videotranslator.ai)

Product Update: How to Set Up a Video Call Tour

A product tour showing how to set up VideoTranslatorAI video calls for live translation, captions, transcripts, and meeting summaries.

How to Set Up a Video Call in VideoTranslatorAI

Real-time interpretation is one of VideoTranslatorAI's most powerful features, and we want every user to feel confident using it from the start. 

The **How to Do an Interpret Tour** walks you through the full interpretation workflow, from launching a session to delivering accurate, real-time output for your audience.

## **What Is the Interpret Tour?**

The Interpret Tour is a guided in-app walkthrough that shows you how to use VideoTranslatorAI's live interpretation mode. 

This feature is built for meetings, interviews, consultations, and events where two or more languages are in use simultaneously. The tour removes the guesswork and gets you to your first live interpretation session in minutes.

## **Tour Overview**

### **Starting the Interpretation Mode**

The tour begins by showing you how to switch from transcription mode to interpretation mode. You just need to click the ‘Interpret’ button located next to the Transcribe button, and you’re ready to go. 

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/88741da3fc933c8f5d6ab0193cc2be93351494843a3b9b4090102d7f7584ca9e.avif)

### **Selecting Meeting Languages** 

Set up the languages that will be used in your meeting. A maximum of two languages can be selected for in-person interpretation and video call mode. 

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/7088099576639e182c982add1178c9c8343f4410de7256873582289a931ea1a2.avif)

### **Selecting Summary Prompt**

The chosen summary prompt will determine the format of your summary output after the call. You can refine your preferred format and tone by creating a customised summary prompt. [**Learn how to build a prompt here →**](https://videotranslator.ai/blog/building-a-prompt-tour)

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/7ca4909f509a12f138e1cb8da14d28b87ebd9569d38e4325586f6d4dde93ebbb.avif)

### **Kicking Off Your First Multilingual Meeting** 

Learn how to activate live audio capture and start real-time interpretation. This tour demonstrates how the interpretation process works, how to stop the session, and how to access the session summary generated afterward.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/f9809d570ced29ab52b79d061d40f0c7e6e8967335781dd884c6b3dfe85df7d1.avif)

## **Goals of This Tour**

*   Build user confidence with live interpretation before their first real session
    
*   Minimise setup errors that affect audio capture and translation accuracy
    
*   Enable faster, more consistent session starts across all use cases
    

## **Who Is This Tour For?**

This tour is designed for professionals using VideoTranslatorAI for client meetings, healthcare consultations, legal interviews, or any setting where live, accurate interpretation matters.

## **Try It Today**

The Interpret Tour is available now inside the VideoTranslatorAI app. Access it from the onboarding flow or at any time via Help > Guided Tours.

Ready to interpret with confidence? [**Open the app and start the tour →**](https://agent.videotranslator.ai)

Product Update: How to Do an Interpret Tour

A product tour showing how to start live interpretation in VideoTranslatorAI and move from setup to translated conversations in minutes.

How to Use VideoTranslatorAI Interpret Mode

We are always looking for ways to make VideoTranslatorAI more intuitive for new and returning users. Today, we are excited to introduce the **Building a Prompt Tour**, a guided walkthrough designed to help you get the most out of our AI-powered summary features from the very first session.

## **What Is the Building a Prompt Tour?**

The Building a Prompt Tour is an in-app guided experience that walks you through how to customise prompts that drive better summary results. This tour makes that process simple, step by step.

## **Tour Overview**

### **Opening the Custom Prompt Setup**

The Prompt Tour begins by showing you how to access the custom prompt builder. Click the 'Custom Prompt Setup' button to open the interface where you will define your summary preferences.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/e442c9db7e6c43b4591ef8b263d2a38b48066426f85a691bee914254e705b4b7.avif)

### **Building Your First Summary Prompt**

Define your desired summary by selecting from pre-set options. Guide the AI by choosing the Session Type (e.g., Team Meeting, Client Pitch), the Formatting Style (e.g., Bullet Points, Narrative), the desired Tone (e.g., Formal, Action-Oriented), etc.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/0c0259f638e58f406ec14b927720425ff3add27e3b3b15a8816d426f45da0e79.avif)

### **Adding Context and Constraints**

Discover how to add contextual details that improve the accuracy of the summary with voice setup. Tell the AI about the speaker roles, topic areas, and any terminology the AI should prioritise or avoid.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/65433ec3d46407f23bf89f9654c0578d444cfb6ca3f8611f5b4f418d7d461973.avif)

### **Previewing and Saving Your Prompt**

Run a quick preview of your prompt output and save it as a reusable template for future sessions.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/5cb0ab09c7d5c5875e32f75abe0b4b3e454b469d29c01fb2c4534288a9fc3e76.avif)

## **Goals of This Tour**

*   Help users build effective, reusable custom summary prompts without technical knowledge
    
*   Reduce summary setup time before meetings, calls, and language sessions
    
*   Give users confidence to customise their experience from day one
    

## **Who Is This Tour For?**

This tour is ideal for first-time users setting up VideoTranslatorAI for the first time, as well as existing users who want to improve the consistency of their translations across sessions.

## **Try It Today**

The Building a Prompt Tour is now available in the VideoTranslatorAI app. You can launch it anytime by clicking on the ‘Take a Quick Tour’ button at the top of your chat. 

Ready to get started? [**Launch the app and begin your tour →**](https://agent.videotranslator.ai)

Product Update: Building a Prompt Tour - VideoTranslatorAI

Learn how VideoTranslatorAI prompt setup helps teams control summary structure, terminology, and outputs from the first meeting.

How to Build Better Prompts for Meeting Summaries

Product Update: Building a Prompt Tour

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

A practical look at Karpathy-inspired LLM notes and how smarter meeting records could improve recall, decisions, and follow-up work.

What Smarter AI Notes Could Mean for Teams

_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

Explore why AI-driven hiring feels broken in 2026 and what job seekers can do to communicate clearly through automated filters.

Applying to Jobs in 2026 and the AI Hiring Loop

OpenAI ran its first-ever Super Bowl ad this year. The spot traced the history of human curiosity and building, from a child chasing a spiderweb to lines of early code on an aging computer, to a modern laptop running Codex. It closed on five words:

**_“You Can Just Build Things.”_**

@[youtube](https://www.youtube.com/watch?v=aCN9iCXNJqQ)

It was not wrong. In fact, now you can.

More people than ever before are building apps, tools, products, and businesses on top of AI. The barrier to creating something that works is lower than it has ever been in the history of software.

Vibe coding, a term Andrej Karpathy coined in early 2025, has moved from developer circles to mainstream awareness. You describe what you want in plain language, an AI builds it, and you iterate from there.

The problem is that “you can just build things” has been enthusiastically taken up by a very large number of people, and most of what they have built is sitting in silence.

No users. No revenue. No traction. The silence that follows a launch has become so common it has its own rhythm: build, launch, nothing, add features, launch again, nothing.

This is not a story about AI making building impossible. It is a story about building never having been the hard part.

## **Is the Market Really Saturated?**

![Photo by GR Stocks on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*dSv459i2syOqUfMW "Photo by GR Stocks on Unsplash")

Spend enough time on Twitter (now X), and you will encounter a recurring anxiety. With vibe coding making it trivially easy to ship an MVP, the argument goes, the market is being flooded with apps.

There are too many tools. Too many AI wrappers. Too many products solve the same vague problems with slightly different interfaces. Everything feels like it already exists, and the window to build anything meaningful has closed.

This fear is understandable. The number of products launched on platforms like Product Hunt has grown dramatically. Certain categories, simple AI writing tools, basic chatbots, generic productivity apps, genuinely are saturated in the sense that another entrant offers no meaningful differentiation.

But the conclusion that the whole market is saturated is wrong in a specific and important way.

Supply is exploding, yes. But demand is also exploding, just in a different way. Entire industries, such as healthcare, legal, construction, agriculture, and aged care, are only now starting to digitise seriously. The problems are enormous. The customers are real. The willingness to pay is there.

However, there’s a gap between supply and _quality_ supply, and between products that exist and products that people can actually find.

## **What AI Actually Exposed**

![Photo by Compagnons on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*liEASYLEbkRe8ZE2 "Photo by Compagnons on Unsplash")

Here is the thing that vibe coding has made brutally obvious, even though it has always been true.

> **_Building was never the hard part._**

The hard parts have always been the same:

**_Understanding people_.** Not users in the abstract. Specific humans, with specific workflows, specific frustrations, and specific things they’d actually hand over their credit card to fix.

**_Getting distribution._** You can build the best product in the world and go completely broke if nobody finds it. Audience, partnerships, SEO, sales skills, virality. These are the skills that move the needle.

**_Building moats AI can’t copy in a weekend._** Proprietary data. Brand trust. A community that relies on you. Regulatory expertise. A personal network built over years. These are the things that compound and can’t be vibe-coded into existence.

The founders who win right now aren’t the best prompters. They’re the ones who deeply understand a specific group of people and have figured out how to reach them.

## **We’ve Seen This Panic Before**

![Photo by Igor Omilaev on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*EX4Tt8_NuN9OBJfw "Photo by Igor Omilaev on Unsplash")

This exact feeling, the dread that everything is saturated and the opportunity is gone, is not new.

It hit when WordPress and cheap hosting made everyone a blogger between 2005 and 2010. Everyone said blogging was over. The people who kept publishing with genuine insight and built real audiences ended up fine.

It hit when the App Store and early no-code tools made everyone a mobile developer between 2009 and 2013. Everyone said the App Store was too crowded. The people who solved real problems for specific audiences built serious businesses.

It hit when no-code tools and Stripe made everyone a SaaS founder between 2018 and 2022. Everyone said SaaS was dead. The people who found genuine niches and learned to sell made money.

Each time, the “it’s saturated” crowd sat on the sidelines. Each time, the people who kept building with taste, obsession, and a distribution strategy ended up rich, or at least fine.

This time is structurally the same. The only thing that’s changed is the **_speed_**.

## **Distribution is the New Moat**

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

Greg Isenberg, founder of Late Checkout and one of the sharper voices in the builder community, put it bluntly in his YouTube video “**_Stop Vibe Coding. Start Getting Customers_**.”

Code is now commoditised. Distribution is the new bottleneck.

He lays out seven concrete strategies for vibe coders and builders who want to actually reach customers. Here’s a summary.

**1\. Use an MCP server as your sales team.** Build a server that answers the questions your product solves, then publish it to MCP registries. Every AI assistant that connects to it becomes a 24/7 sales rep for you. (Note: this one’s technical, and not relevant for every product.)

**2\. Programmatic SEO.** Generate 10,000 pages in a weekend using a keyword pattern, a dataset you scrape with something like Firecrawl, and a page template. Think “AI interpreter for meetings” and “real-time interpreter in Sydney.” Just make sure a human reviews the content, otherwise it turns into slop fast.

**3\. A free tool as your top of funnel.** Build a grader, analyser, or calculator that gives users instant, shareable value. Their score becomes social proof. Their shares become backlinks. You capture their email. Then you upsell.

**4\. Answer engine optimisation.** Get cited by ChatGPT, Perplexity, and Google AI. Write clear, structured, direct answers to the top 20 questions your customers ask. Add FAQ schema markup. Monitor your citations using a tool like [otterly.ai](http://otterly.ai).

**5\. The viral artefact.** Make your product’s output shareable. Spotify’s Wrapped. GitHub’s contribution graph. Duolingo’s streak count. What does your user want to brag about? Build a beautiful, shareable card and add a pre-filled share button. Your users become your marketing team.

**6\. Buy the audience.** Building an audience from zero takes years and isn’t guaranteed. Instead, acquire a niche newsletter. Go to [Duuce.com](http://Duuce.com) or Newsletter Investor, find newsletters with 5k to 50k subscribers in your target niche, and DM the owner. Many are making next to nothing and will sell for $5k to $20k.

**7\. One pillar, seven channels.** Record 30 minutes of content once a week. Transcribe it. Drop the transcript into Claude and turn it into tweet threads, LinkedIn posts, short videos, a newsletter, a blog post, and quote graphics. Schedule across platforms. The key: set up your prompts carefully so the output doesn’t sound like AI slop.

The core insight running through all seven: most vibe coders are building in distribution-first order. They build first, then hope people find them. Smart builders flip it. They grow an audience first, ask that audience what they need, build it in a weekend, then launch to people who are already waiting.

## **What This Means for You**

The opportunity in 2026 is not about who can build the fastest. That race is over and AI won.

The opportunity is about who understands a specific group of humans deeply enough to build something they actually want. And who has the channels to reach them.

If you can do those two things, the collapsing barrier to building is not a threat. It’s the best thing that’s ever happened to you. You can now test ideas in days that used to take months. You can serve niches that were too small to justify the engineering cost. You can build, learn, and iterate faster than any previous generation of founders.

The market isn’t saturated. It’s just that most of the new supply is solving the wrong problems for people they’ve never actually talked to.

Talk to people first. Build distribution. Then build the thing.

That order has always mattered. It just matters more now.

* * *

_Greg Isenberg’s distribution framework is presented in his YouTube video “Stop Vibe Coding. Start Getting Customers,” The ideas in that section are his, and the original video is worth watching in full. OpenAI’s Super Bowl LX ad, “You Can Just Build Things,” aired in February 2026 and was produced by Doomsday Entertainment._

Why Your AI App Has No Users (And 7 Ways to Fix It)

Why Your AI App Has No Users (And 7 Ways to Fix It) - Tat Banerjee

Seven practical distribution lessons for AI builders trying to reach real users, explain value, and move beyond product-only thinking.

Why Your AI App Has No Users and How to Fix It

There’s a specific feeling that hits when you open the internet on a Monday morning and realise the tool you spent last week learning has already been overtaken by something new.

You close the tab. You open it again. You read the announcement, feel a quiet panic, and wonder:

> _am I already behind?_

You’re not alone in that feeling. Not even close.

In late 2025, Andrej Karpathy posted something on X that stopped a lot of people mid-scroll (including me). If you’re not familiar with his name, let me tell you this: Karpathy isn’t a casual observer of the tech world. He’s one of the founding researchers at OpenAI, former AI lead at Tesla, and one of the most respected voices in artificial intelligence. If anyone should feel on top of things, it’s him.

But here’s what he wrote:

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/a8fc0eb7f1293fc12e83e02bbca0a64ccc13adf8fbd58a302fc06c9b27638d29.avif "Source: X")

That post got more than 32,000 bookmarks. Sixteen million people saw it.

The number matters. It tells you how many people read those words and thought: _yes, that’s exactly it._

## **Why This Moment Feels Different**

![Photo by Guillaume Jaillet on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*nTKcuBOauYmsP1Fp "Photo by Guillaume Jaillet on Unsplash")

Technology has always moved fast. People have always had to adapt. So why does this particular moment feel so destabilising?

The answer has to do with how the rules changed, not just the speed.

Traditional software and knowledge work operated on a fairly reliable assumption: **more effort in, more output out.** If you put in three hours, you could roughly predict what three hours of work would produce.

The relationship between input and output was mostly linear, and your mental model of how to do the work remained stable for long stretches of time. You learned a skill. The skill worked. You used the skill.

But AI inverts this in ways that take a while to fully internalise.

You now start from a high-level intent, something like “build me a tool that does X” or “draft a strategy for Y,” and an AI model jumps straight to a generated result. Your job shifts from doing the work to directing the work, and then verifying whether the result is actually good.

This is a fundamentally different cognitive loop. It is faster in some ways and more disorienting in others, because the quality of what comes out depends less on your technical execution and more on how well you can articulate what you actually want, how well you can spot errors in output you did not produce yourself, and how robust your system is for catching mistakes before they matter.

On top of that, the tools themselves keep changing. A model that was state-of-the-art six weeks ago may now be outperformed by something new. A workflow that worked beautifully last month may need to be rebuilt because the underlying model behaves differently. Your mental model of what is possible, and what is reliable, starts decaying the moment you form it.

[Nate B Jones,](https://www.youtube.com/@NateBJones) who runs the YouTube channel AI News & Strategy Daily, made a video directly responding to Karpathy’s post. In it, he put the situation plainly:

> **_“If you haven’t played with \[the latest model\]… your world model is already outdated.”_**

That’s not meant to be scary. It’s meant to be clarifying. The anxiety you feel isn’t weakness or incompetence. It’s a rational response to a genuine phase shift.

## **Not a Problem of Laziness or Intelligence**

![Photo by Nubelson Fernandes on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*Kf3l_T97UR9jLAJP "Photo by Nubelson Fernandes on Unsplash")

Before getting to the skills that actually help, it is worth dwelling on why this anxiety tends to come dressed as a personal failing.

Karpathy called his own gap a “skill issue.” Boris Cherny, the guy behind Claude Code, responded to Karpathy’s post by saying he feels this way most weeks.

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/0f916853072837ef52b3ad74d255b8da06a2dabf935ae0dd98a15350dd957795.avif "Source: X")

When people whose entire careers are built around AI feel perpetually behind, the idea that ordinary professionals in law, marketing, education, or healthcare should feel comfortably on top of it is simply not realistic.

The anxiety is not a sign that you are failing. It is a sign that you are **paying attention.** The transition happening right now is genuinely discontinuous. It is not like learning a new version of software you already know. It is more like being handed a powerful new tool without an instruction manual, and watching the tool itself change shape every few weeks while you are still reading the first chapter.

The goal is not to eliminate the feeling of being behind. That feeling is probably going to be with us for a while. The goal is to build the right kind of capabilities, the ones that remain useful even as the specific tools keep changing.

## **So What Do You Actually Do about It?**

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

In this video, Nate lays out what he calls a skill tree for working effectively in this new environment. It’s worth giving him full credit here: this framework comes from **_his_** thinking, and it’s one of the more practical maps I’ve come across for navigating the current moment. The internet runs on people sharing good ideas, and this is a good one.

The skill tree has four levels.

### **Level 1: Conditioning (Intent, Context, Constraints)**

This is the foundation. Before you can use AI tools well, you need to get good at communicating. Not in a vague, hopeful way, but with precision. What do you actually want? What context does the model need to produce something useful? What constraints should limit its output?

Think of it like giving instructions to a very capable new colleague who knows a lot but doesn’t know your situation, your standards, or your preferences. The more clearly you can articulate those things, the more reliable the output becomes.

This isn’t a technical skill. It’s a thinking skill. It applies whether you’re a lawyer drafting a brief, a marketer writing copy, or an engineer building a feature.

### **Level 2: Authority (Verification, Provenance, Permissions)**

Once a model generates something, someone still has to decide whether it’s right.

This level is about keeping that decision-making responsibility clearly in human hands. Generation is what the AI does. Decisioning is what you do. Keeping those two things separate is how you stay accountable for outcomes, not just inputs.

Practically, this means building habits of **verification**: checking sources, testing outputs, creating a trail of evidence so you can explain why something was approved. It also means thinking about access and permissions. What should the AI be able to touch? What should require a human sign-off?

The less visible this level is in your workflow, the more exposed you are when something goes wrong.

### **Level 3: Workflows (Pipelines, Failure Modes, Observability)**

Individual prompts are the beginning. At some point, the work gets complex enough that you need systems, not just conversations.

This level is about designing those systems deliberately. Breaking work into stages. Defining what success looks like at each checkpoint. Anticipating what can go wrong (not if, but when) and building in the ability to detect and recover from those failures.

Observability is the technical word for this, but the concept is simple: can you see what’s happening inside your process? If something breaks, can you tell where it broke and why? The bigger the system, the more important this becomes.

### **Level 4: Compounding (Evals, Feedback Loops, Governance)**

The fourth level is where people start to separate. Most people use AI tools in a way that resets every time. They get a result, move on, and start again from scratch next time.

Compounding means building in ways for your process to improve over time. Evaluation frameworks that tell you whether outcomes are getting better or worse. Feedback loops that route that information back into your workflow. Version control and governance so you can understand what changed when results shift.

This is how leverage grows instead of decays. It’s also the level that most people skip because it requires up-front investment with delayed payoff.

## **Who Does This Apply To?**

![Photo by Studio Republic on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*wCiIp5Wr6sCANeUo "Photo by Studio Republic on Unsplash")

The skill tree is universal in principle, but what each level looks like in practice varies enormously by domain. It is worth thinking through some of those differences, because the transition is genuinely smoother in some fields than others.

**_Software engineering_** has the most direct feedback loops. Code either runs or it does not. Tests pass or they fail. This makes Level 2, the verification and authority level, relatively tractable: you can automate checks that catch AI errors before they ship. The transition has still been disorienting, because the nature of the programmer’s contribution has changed, but the field has built-in error detection that other domains lack.

**_Marketing and creative fields_** have a complex relationship with Level 1. The conditioning skill, learning to articulate what you actually want, maps directly onto skills that strong creatives already have. Good creative directors have always needed to brief people precisely. The challenge is that AI output can look polished enough to feel finished when it is not, and the markers of quality are more subjective, which makes Level 2 harder. You cannot run a unit test on a brand voice.

**_Law_** faces a specific version of the authority problem. AI can produce legally plausible text at impressive speed. But legal work depends on precision, jurisdiction-specific accuracy, and professional accountability that cannot be delegated to a system that sometimes confidently produces errors. Level 2 and Level 3 are not optional in this domain. They are existential. The lawyers who will thrive are the ones who use AI to accelerate research and drafting while maintaining rigorous verification and clear accountability trails. The ones who use AI as a shortcut without the verification layer are accumulating risk that will eventually surface.

**_Medicine_** is similar but the stakes are even higher and the feedback loops are slower. AI can summarise research, support diagnosis, draft patient communications, and flag patterns in data. The error consequences in clinical settings mean that Level 2 is not just professionally important but ethically non-negotiable. The interesting opportunity is at Level 4: medicine has excellent traditions around evaluation and evidence-based practice that translate well into building the kinds of AI governance frameworks that compound over time.

**_Education_** may see one of the more interesting transitions. Teaching has always been about meeting people where they are, adapting explanations to context, and building on what someone already understands. These are deeply conditioning-level skills. Educators who embrace AI as a way to personalise learning materials, generate examples tailored to specific students, and create feedback loops around what is working may find the transition more natural than in fields that are more execution-heavy.

The domains where the transition is most disruptive are generally those where the value was historically created by effort and volume, things like producing large quantities of copy, conducting routine data analysis, or generating standard documentation, rather than by judgement, accountability, or the kind of trust that comes from professional relationships. Those domains are seeing the most direct compression of what humans need to do, which is uncomfortable but also clarifying.

## **What to Do With the Anxiety**

![](https://miro.medium.com/v2/resize:fit:1260/1*fNCNC8JKL-ULoEBlNS3FNw.png)

Generated by Canva AI

Here is the most useful reframe I have found for the feeling of being perpetually behind.

Being behind on specific tools is fine. Being behind on the underlying skills is not. The difference matters because tools change weekly and skills compound over years.

If you are spending most of your anxiety energy trying to keep up with every new model release and every new feature announcement, you are running on a treadmill that will never stop.

If instead you are putting that energy into getting better at conditioning, at building verification habits, at designing workflows and evaluation frameworks, you are building something that will serve you regardless of which specific tool is current next month.

In his reply to Boris Cherny’s tweet, Karpathy wrote an optimistic note. He described the feeling of occasionally holding the tool at just the right angle and watching a laser beam solve the problem instantly. That experience, he said, makes the effort worth it.

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/58ebcc94fbb5944ef692b724bd9f3c6dabf87ce6b38e586fd4e2cf96471131a4.avif "Source: X")

The people who are going to have that experience most reliably are not the ones who have memorised the most settings. They are the ones who have built the deepest understanding of how to work with AI systems effectively, at a level that transfers as the systems change.

The anxiety is real. The pace is real. But the skills that matter are learnable, and the people who start building them now will compound their advantage over everyone who waits until things feel more settled.

They will not feel more settled. The time to start is now.

* * *

_Nate B Jones’ video “Why Andrej Karpathy Feels ‘Behind’ (And What It Means for Your Career)” is published on his YouTube channel AI News & Strategy Daily. The skill tree framework and the quote used in this article are his work, shared here because good ideas deserve wider reach._

Everyone Feels Behind in AI. Even Andrej Karpathy.

Everyone Feels Behind in AI. Even Andrej Karpathy. - Tat Banerjee

A clear look at the AI learning curve, why even experts feel behind, and which practical skills matter most for using AI well.

Why Everyone Feels Behind in AI Right Now

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

The OpenClaw refund dispute highlights a growing accountability problem as AI agents begin making purchases and decisions for users.

When AI Agents Spend Money, Who Is Responsible?

Language barriers don’t announce themselves. They show up quietly: a tourist frozen at a train station counter, a receptionist reaching for a paper map because words have run out, a parent overseas trying to follow along in a school meeting conducted entirely in a foreign tongue.

These moments are small. But they add up.

And they’re more common than most people realise.

## **The Gap Nobody Talks About**

![Photo by Mihaela Claudia Puscas on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*p0ctjlK7JXNLkzK7 "Photo by Mihaela Claudia Puscas on Unsplash")

Around 15.2 per cent of tourists name difficulty communicating with facility staff as a top inconvenience during their stay.

That’s not a minor footnote. That’s nearly o**ne in six travellers** leaving a destination less satisfied than they should be.

In Japan alone, over 36.8 million visitors arrived in 2024. Tourist Information Centres (TICs) are meant to be the welcoming front door.

But many TIC staff, despite years of grammar study, rarely get the chance to practise real conversation. The result? Visitors walk away with incomplete directions, missed recommendations, and a quiet frustration that colours the whole trip.

Rural communities feel it hardest. Not because there’s nothing to offer, but because the path from “I want to visit” to “I know how to get there” gets lost somewhere between two languages.

![Photo by Patricia Haller-Anguela on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*Nze8BVtfJoqHNegi "Photo by Patricia Haller-Anguela on Unsplash")

## **Meeting People Where They Are**

This is the problem [VideoTranslatorAI](https://videotranslator.ai/) was built to solve.

The AI [in-person translator](https://videotranslator.ai/product-overview/in-person) works at the desk, in real time. A receptionist asks a question in Japanese. The visitor hears it in their own language. They respond. The conversation flows, naturally, without fumbled phrases or awkward pauses.

Subtitles appear on screen. Voice-over adds another layer of clarity. Nobody needs to slow down, repeat themselves, or reach for a translation app that half-works.

Staff feel confident. Visitors feel heard.

That’s a different kind of hospitality we’re trying to build.

![Illustration: seamless Japanese-Hindi conversation with VideoTranslatorAI interpreter tool](https://miro.medium.com/v2/resize:fit:1260/1*V3xUa6ZgfvaZ-8WhkEKDpw.png "Illustration: seamless Japanese-Hindi conversation with VideoTranslatorAI interpreter tool")

## **Before They Even Arrive**

The language gap doesn’t start at the information desk. It starts months earlier, when someone overseas is trying to book a ryokan, confirm dietary requirements, or ask whether a certain experience is suitable for elderly parents.

The [Multilingual Video Call](https://videotranslator.ai/product-overview/video-call) feature handles exactly that. Hospitality providers speak in their own language. Travellers listen and respond in theirs. The AI translates in **_real time_**, on **_both_** sides.

![Illustration: multilingual video call with VideoTranslatorAI](https://miro.medium.com/v2/resize:fit:1260/1*3UdJELj2enp5k3xsvHI4vw.png "Illustration: multilingual video call with VideoTranslatorAI")

Itinerary details get confirmed accurately. Special requests don’t fall through the cracks. Trust builds before the guest ever sets foot on the property. And providers don’t need to hire multilingual staff to make it work.

## **One Tool, Many Conversations**

![Photo by CHUTTERSNAP on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*QvTBICewmNq44o7t "Photo by CHUTTERSNAP on Unsplash")

What makes this genuinely useful rather than just interesting is the range. The same technology that helps a tourist at a TIC desk is already bridging a different kind of communication gap entirely.

For families who’ve sent children to study abroad, staying connected with teachers and school staff is its own challenge. Time zones, language differences, and the formality of school meetings can make parents feel locked out of their child’s education.

**Read how VideoTranslatorAI is helping parents and teachers connect across borders**: [**"Yes, Okay, Thank You" and Your Child Just Failed Another Grade**](https://videotranslator.ai/blog/yes-okay-thank-you-and-your-child-just-failed-another-grade)

Same pain point, different setting. The tool meets it in both.

## **How It Works**

The process is straightforward enough that you don’t need a manual.

**Step 1: Choose your mode**

Select whether you want to transcribe (capture spoken words as text) or translate (convert between languages in real time).

![Choose your mode](https://miro.medium.com/v2/resize:fit:846/1*N-gq9tTd4jzoeBqmeScb3Q.png "Choose your mode")

**Step 2: Select your language**

Choose the source and target languages from the available options.

![Select your language](https://miro.medium.com/v2/resize:fit:1260/1*zD1KwZBqpGGobdmaxMrnFQ.png "Select your language")

**Step 3: Customise your summary prompt**

Tell the tool what you need from the summary. This is where you tailor the output to your context, whether that’s a guest inquiry, a booking call, or a parent-teacher meeting.

![Customise your prompt](https://miro.medium.com/v2/resize:fit:1260/1*OAj9s70QlnuHLggbAVyWDw.png "Customise your prompt")

**Step 4: Start**

Hit start. The tool runs in the background while the conversation happens naturally.

![Live interpret session with VideoTranslatorAI](https://miro.medium.com/v2/resize:fit:1260/1*eJaYqpL2J6o06hto_4TT6Q.png "Live interpret session with VideoTranslatorAI")

**After the session**

Once the meeting wraps up, download the summary or send it directly to your email.

![](https://miro.medium.com/v2/resize:fit:1260/1*Joq6zBfZktRMQS2fuM-vcQ.png)

That’s it. No complex setup. No technical training required.

## **What This Actually Changes**

![Photo by Justin Lim on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*jWozXD9rQDcXMUG3 "Photo by Justin Lim on Unsplash")

The tourism industry has invested heavily in experiences. The food, the design, the culture, the craft of service.

What it hasn’t always been able to invest in is the last few metres: the moment of communication between a staff member and a visitor who don’t share a language.

AI translation doesn’t replace that human warmth. It gives it somewhere to go.

For Japan, a country whose hospitality is legendary but whose language barrier is just as well known, that’s not a small thing.

It’s the difference between a visitor who leaves saying “it was beautiful” and one who leaves saying “and everyone made me feel so welcome.”

Language should never be the thing that gets in the way of that.

* * *

_VideoTranslatorAI is available for tourism operators, hospitality providers, educational institutions, and anyone navigating multilingual communication in real time._

Rural Japan Has Incredible Experiences Tourists Can’t Access

1 in 6 Tourists Leave Frustrated. The Problem is Language - Tat Banerjee

Explore how language barriers limit rural Japan tourism and how AI translation could help visitors access local experiences.

Rural Japan Tourism Needs Better Language Access

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://chat.google.com/u/0/api/get_attachment_url?url_type=FIFE_URL&content_type=image%2Fpng&attachment_token=AOo0EEXsS7ak7QxIc1hKQVNPU6IMjGkyWLONWE3LXcuDGUssKYYnveFTi23c0BL%2B5c%2B6WP7NUKn8DCWFtj4bHDNXdEAPDAY20OOKYlGlkzt3PYlMS%2FBUFlWBLyOsfdq0VZyU9AIYduOuTi9B2mpoYYYTOGonrFmb1s7NplOKHW4vVKyEdAXWAz0a4BCBUvT4O70Y8fzK71H08tRMlHj7qoH8dD8e7%2Fokjhy3%2FFGVDc32%2FUbEDNTjQegYWjFWkdUBoLB96UFZxwQyOKA0EIJi9kmzZWpxjeUEHdwmf1dq58jMuiK551KIkcLOPGoETOlafYFbC3Pi2S2tTf9qrbV77uTbi9faktGADBjg%2Fy8fNLnGhhdeRZ4pxaoMReIPmU4I6BpWI1zzNQr8lwB3UCsYBlWP5bay9Tx8B6LbLchz1MDotkKkx5W%2BrNPipuLwgDRWvVojqgvziEBE0nkDqSULFAmCtHWf4OTPhMGiiVxk814nVwmE%2BoPb27j5IzOlxu1jky2DDT9zysyMm8EV5bdROAAzQJcbwMDcuEHcRS9oo0uNj%2BibVKeCeyj%2BmyelDQ4woNc6rkZeV0F1nMaoM30aerwL&allow_caching=true&sz=w512 "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://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/515306c035650b232aa48c3dd6cd0aa886b97f4765931aaab8248fc4919020ee.avif "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**

![Source: OpenClaw.ai](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/a368fc9137555a7fcdeaba610bc42335fed6144b386e69ed0b1638a0663a8acd.avif "Source: OpenClaw.ai")

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://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/9c2062e3330e4d5122b87523789b9ef2cdd7d6e5f797f9694799fcb797bf31d7.avif "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://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/181a680d6252db323bba44ffa23cb3dffdb78fcfb5004cb317710df865ea1cde.avif "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 and interview mockup app! ](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/648492b030317234245fb26e503cf77de9995bae184b1a84d3d014949f569dfc.avif "JobsLobster: Our upcoming resume builder and interview mockup 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://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/ed835b4bd7515d88e854071ed4c8a550ed2e728fc659e1fd2baf878f1fe0db16.avif "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

Understand the shift from reactive apps to agentic AI experiences that anticipate needs, initiate work, and reshape consumer software.

Why Agentic AI Apps Are Replacing Reactive Tools

It’s the summer of 2016.

You’re in a park at 11 pm, phone raised at a slightly embarrassing angle, pointing your camera at a war memorial you have walked past a thousand times without a second glance.

You are not sightseeing. You are catching a Snorlax.

Or maybe it was 2021 and you were scanning a fountain for Field Research rewards because someone promised there was a Shiny Magikarp nearby.

You were not thinking about delivery robots. You were thinking about Pokémon.

Turns out, you were doing **_both_**.

## **How a Game Became the World’s Best Mapping Project**

![Source: Pokémon Go](https://cdn-images-1.medium.com/max/1440/0*QhNuuL7FzCKQkWnm "Source: Pokémon Go")

On 10 March 2026, a company called Niantic Spatial announced a partnership with Coco Robotics. It’s a startup that operates small suitcase-sized delivery robots across Los Angeles, Chicago, Miami, Jersey City, and Helsinki.

Coco robots trundle along footpaths at about 8 kilometres per hour, delivering food and groceries. They have completed over 500,000 deliveries.

And they are now navigating the world using a map built almost entirely from **Pokémon Go** data.

[John Hanke](https://www.technologyreview.com/2026/03/10/1134099/how-pokemon-go-is-helping-robots-deliver-pizza-on-time/), CEO of Niantic Spatial, put it plainly: “_It turns out that getting Pikachu to realistically run around and getting Coco’s robot to safely and accurately move through the world is actually the same problem_.”

That sentence deserves a moment.

![CEO of Niantic Spatial, John Hanke (Source: X)](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/3e7b1518246b8ae944337f79f4cb79765540c155e397b5f29adc01c383255507.avif "CEO of Niantic Spatial, John Hanke (Source: X)")

The same underlying technology that made a virtual cartoon creature appear to stand on your actual footpath is now the reason a robot can find your front door within a few centimetres of accuracy. The same scans of statues and street corners that unlocked your in-game rewards have been stitched into one of the most detailed ground-level maps of the physical world ever assembled.

Over a decade, Pokémon Go players voluntarily submitted photos and short videos of public landmarks, street corners, storefronts, and urban intersections, creating a dataset that now stands at 30 billion images captured at ground level, across nearly every major city on the planet.

**30 billion**. That is not a typo.

## **Why GPS Alone Is Not Enough**

![Pokemon Go (Photo by John Hanke via X)](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/52dc65523f27153495c5fc17230135c3945d9db3d1b0a38723b6fc34adfb5d17.avif "Pokemon Go (Photo by John Hanke via X)")

If you have never thought about why a delivery robot might struggle to find your exact doorstep, here is the unglamorous reality of GPS in a city.

GPS, the backbone of most navigation systems, does not fare that well in dense urban environments, where tall buildings interfere with satellite signals. For a delivery robot that needs to drop food at a precise doorstep, being several feet off means unhappy customers.

Niantic Spatial’s solution bypasses satellites entirely. Their **Visual Positioning System**, or VPS, works differently: the robot’s onboard cameras capture what is around it, and the system matches those live images against its vast database to determine the robot’s exact location in real time.

Coco’s robots use four cameras mounted around the machine, constantly comparing what they are seeing against the archive of images that millions of players contributed over eight years.

“We know where you’re standing within several centimetres of accuracy and, most importantly, where you’re looking,” says [Brian McClendon](https://www.technologyreview.com/2026/03/10/1134099/how-pokemon-go-is-helping-robots-deliver-pizza-on-time/), CTO at Niantic Spatial.

The Pokémon Go data is particularly valuable because of how it was collected. For each of its million-plus mapped locations, Niantic Spatial has thousands of images taken in more or less the same place but from different angles, at different times of day, and in different weather conditions. Each image comes with detailed metadata that pinpoints where the phone was in space at the time of capture, including which way it was facing, which way up it was, and whether it was moving.

No traditional mapping company could have assembled that on the same timeline or budget. Google Maps gives you a street-level photo from a camera car that drove past once. Pokémon Go gave Niantic the same spot photographed from hundreds of angles, across every season, in rain and sunshine, by people standing at different heights, on different days.

That richness is what makes the dataset genuinely irreplaceable.

## **The Bigger Picture: Gaming as the World’s Biggest Data Farm**

![Source: Coco Robotics](https://cdn-images-1.medium.com/max/1440/0*t2BKM7uxpar-0Lr3 "Source: Coco Robotics")

The Coco partnership is just the opening act. What Niantic Spatial is actually building is considerably more ambitious.

Hanke calls it a _“living map”_.

What he refers to is a hyper-detailed virtual simulation of the world that changes as the world changes. As robots from Coco and other firms move about, they will provide new sources of map data, feeding into more and more detailed digital replicas of the world.

Think about what that means. The Pokémon Go players built the foundation. Now the robots themselves become the next generation of data collectors, continuously updating the map as streets change, buildings are renovated, new signage goes up, construction begins. The map learns in real time, from every delivery.

Hanke and his team see maps as shifting in their fundamental purpose. Maps have long been used to help people locate themselves in the world. But maps for machines may need to become more like guidebooks, full of information that humans take for granted, with every object tagged with a list of its properties.

The vision extends well beyond delivery bots. Niantic Spatial has flagged AR glasses as a future application: if you are wearing augmented reality glasses and want digital information to lock precisely onto the physical world in front of you, you need exactly the kind of centimetre-accurate positioning that the VPS provides. The same technology could serve autonomous vehicles, construction robots, accessibility tools, and applications that have not been invented yet.

Niantic Spatial describes itself as “the most ambitious mapping operation ever assembled, one that was funded entirely by its own users’ enthusiasm for catching digital creatures.”

That is quite a sentence to put in a press release. But it is also, technically, accurate.

## **Did Anyone Actually Consent to This?**

This is where it gets spicy, and fair enough.

The opt-in was explicit, technically. Players who submitted scans agreed to Niantic’s terms of service, which allowed the company to use that data. Nobody forced anyone to scan a single landmark. In that narrow legal sense, consent existed.

But **_informed consent_** _i\_s a different question. When you pointed your phone at a bus stop to earn some in-game currency, were you thinking: “I_’m contributing to a commercial AI dataset that will eventually power Sam Altman-backed delivery robots\_”? 

Almost certainly not. You were thinking about the Shiny Charizard.

The reaction online has split roughly down the middle. Half the internet is genuinely impressed. The technical feat of turning a mobile game into the world’s most detailed ground-level map is, objectively, kind of brilliant.

Reddit’s r/technology summed up the pragmatic view: Niantic had no trouble selling its gaming division (Pokémon Go now belongs to [Scopely](https://www.scopely.com/), which has links to Saudi-backed investors), but held tightly onto the spatial AI division. Because the data was always the product.

The other half? Less charitable. “Nothing is sacred,” ran one headline. Players in communities across Reddit and X described feeling like unpaid data labourers. The phrase doing the rounds: “I was a data cow.” Some noted the added strangeness that the Saudi-linked acquisition of Pokémon Go means the game itself is now separated from the company that profited most from its players’ contributions.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d72ac489458fa41652ced06add19ca784e44c0959801000fd3e6c02fb6e383a7.avif)

To be fair: this is meaningfully different from the opaque, invisible data harvesting that social media platforms have normalised. Players actively participated. The scans were deliberate. Nobody was scraping your private messages or tracking you without any indication.

But the gap between “I consented to collect AR data for gameplay purposes” and “I helped build a commercial robotics business” is wide enough to make the discomfort legitimate.

The straightforward fix, going forward: games and apps that collect spatial data for downstream commercial use should say so plainly. Not buried in Terms of Service. Not implied by a broad privacy policy. A clear line: “Your scans may be used to train AI systems, including commercial products.”

That’s it. Not complicated. Just honest.

## **Gotcha, Capitalism!**

Here is what is genuinely remarkable about this story, underneath the ethical complexity.

Five hundred million people installed Pokémon Go in its first 60 days. For years, those players wandered through parks, high streets, shopping centres, and suburban footpaths, pointing their phones at the physical world in exchange for virtual creatures. They built, entirely through the momentum of play and without any grand coordinating plan, the largest crowdsourced ground-level map in human history.

That map now lets a small robot find your front door within a few centimetres, in the rain, at night, in a city full of tall buildings bouncing satellite signals in every direction.

As Hanke put it: “If robots are ever going to assimilate into the environment in a way that’s not disruptive for human beings, they’re going to have to have a similar level of spatial understanding.”

The next time a Coco robot zips past you on the footpath carrying someone’s sushi, it is navigating using a map that millions of people built while chasing Pikachu. Give it a wave. The trainers earned it.

Gotcha, capitalism.

* * *

_So here is the question worth putting to you directly: if you had known, back in 2016 or 2021, that your scans were building a commercial robot navigation system, would you still have played? Would you have scanned differently? Or would it have mattered at all?_

_Drop your answer in the comments. Genuinely curious where people land on this one._

You Trained Delivery Robots While Playing Pokémon Go. Unknowingly.

You Trained Delivery Robots While Playing Pokémon Go. Unknowingly. - Tat Banerjee

A look at how location games, massive image datasets, and user behaviour helped shape real-world robotics and AI mapping systems.

How Pokemon Go Players Helped Train Delivery Robots

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