We are excited to release the newest version of VideoTranslatorAI, and we think you will notice the difference the moment you open it.

We have given the whole interface a major update. The goal was simple: take away everything that gets between you and the conversation. We hope you love the simpler version as much as we do.

## **A Clearer Home**

The home screen no longer has a crowd of buttons competing for your attention. Now there are just two choices: **In-person** or **Video call**.

Pick the one that matches your meeting, and you are on your way.

![VideoTranslatorAI new homepage](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/dc467e1f3a39d4a09fec5451239777144901466fdbe2775224a626ee62ffa43c.avif)

## **In-Person: Two Quick Choices**

If you choose In-person, the next step is short. Select the language that will be spoken, then decide whether you want voice-over turned on. That is it. Your session starts straight after.

![VideoTranslatorAI In-person meeting setup](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/542226ecd5b11d77888f8974517515b6547e7c425c55a6f039eeebdb639b5447.avif)

No long setup. No menus to hunt through. Just the two things we genuinely need to get you started.

## **Video Call: Add Your Guest and Send the Invitation**

Video call follows the same simple pattern. In this mode, you can add up to 10 guests, each speaking a different language.

Enter your guest's details, including their email address and the language they use, then send the invitation. VideoTranslatorAI takes care of the rest.

![VideoTranslatorAI video call setup](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/154866b150d5eae209c6197b9938c08c29f892d1d57a89c9f0499cb918311793.avif)

Your guest simply opens their link and joins. Live subtitles and optional voice-over are ready for the call.

## **Why We Simplified**

When we looked at how people actually used the app, one thing stood out. The best moments happen in the conversation, not in the setup. 

So we asked ourselves a simple question: **_what is the least we need to know to get this conversation started?_**

The answer turned out to be much shorter than we expected. Every extra button and step we removed meant one less thing standing between you and the person you are talking to. 

The result is an app that is easy to pick up whether you are opening it for the first time or using it every day, and that keeps the focus on the people talking rather than the settings.

## **Try the New Version**

The updated interface is live now. Sign up today and get 15 minutes of free credit to try it for yourself. Choose In-person or Video call, and see how quickly you can be in your first multilingual conversation.

[**Sign up today and get 15 minutes free →**](https://app.videotranslator.ai)

* * *

_We would love to hear what you think of the new design. Share your feedback with us by clicking_ [_here_](https://videotranslator.ai/contact)_._

Our Biggest Update Yet Is About Taking Things Away

We simplified VideoTranslatorAI from the ground up.

We simplified VideoTranslatorAI from the ground up. We are excited to release the newest version of VideoTranslatorAI, and you will notice the difference the moment you open it...

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

Most people will get through today without knowing it is International Translation Day. They will send emails, sit in meetings, watch the news, and never once think about the invisible layer of human effort that makes all of it possible: the ability to understand each other across languages.

That invisibility is, in a way, the highest compliment a translator can receive. When the work is done well, you never notice it. You just understand.

Today, 30 September, we stop to notice. And to say thank you.

## **The Man Who Started It All**

![Saint Jerome, Patron Saint of Translator. Generated by Gemini](https://miro.medium.com/v2/resize:fit:1400/1*FStsHGcw_zaEPqlQPlFpxw.jpeg "Saint Jerome, Patron Saint of Translator. Generated by Gemini")

Around 382 AD, a scholar named Jerome sat in a cave near Bethlehem and began what would become one of the most consequential translation projects in human history. Working from Hebrew and Greek manuscripts, he rendered the entire Christian Bible into Latin, the common tongue of the Roman world.

The result, the Vulgate, shaped Western civilisation for over a thousand years. Jerome did not just translate words. He made ideas accessible. He brought meaning across a cultural and linguistic divide that would otherwise have kept an entire body of thought locked away from most of humanity.

International Translation Day falls on 30 September, his feast day, to honour that legacy. The United Nations formalised the observance in 2017, recognising the role of professional translation in connecting nations, facilitating dialogue, and strengthening global cooperation.

It is a day most people have never heard of, and it probably should not be.

## **From Scrolls to Simultaneous Booths**

![Photo by Mark Rasmuson on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*A0SH_RMHQTk9Zfu5 "Photo by Mark Rasmuson on Unsplash")

For centuries after Jerome, translation remained a slow and solitary craft. Scholars bent over manuscripts. Diplomatic interpreters worked in sequence: a speaker paused, the interpreter translated, the conversation resumed. The rhythm was deliberate, even stately.

The 20th century changed that. The Nuremberg Trials of 1945 are widely credited as the moment simultaneous interpretation entered the modern world. For the first time, interpreters worked in real time, listening and speaking at once across four languages: English, French, Russian, and German. The logistics were staggering. The stakes were absolute.

Simultaneous interpretation then spread to the United Nations, to international summits, to global business. Professional interpreters became elite practitioners, trained for years to hold two streams of language in their minds at once without losing the thread of either.

The craft demanded extraordinary human skill. And it still does.

## **The New Frontier: Live Interpretation at Scale**

![Photo by Matthew TenBruggencate on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Ab932xZR14_Xn2E- "Photo by Matthew TenBruggencate on Unsplash")

Technology is now trying to solve this challenge: there are roughly 7,000 languages spoken in the world today, and professional interpreters exist for only a fraction of them.

In countries like Australia, where communities from over 200 countries call home, the gap between the languages people speak and the services available to them is measured not in words but in outcomes.

A patient who cannot explain their symptoms clearly. A family who cannot understand a discharge plan. A community who cannot read an emergency warning. These are not abstract problems. They are situations where language barriers cost lives.

AI-assisted live interpretation is not a replacement for human translators. It is an attempt to reach the places human translation has never been able to reach, at the speed the situation demands.

## **What VideoTranslatorAI has Been Building**

![VideoTranslatorAI’s booth at NSSN Women in Sensing Summit 2025](https://miro.medium.com/v2/resize:fit:1400/1*3UPCwhK4c9EKOi_yXS7ccA.avif "VideoTranslatorAI’s booth at NSSN Women in Sensing Summit 2025")

Over the past several years, VideoTranslatorAI (a product of ElephantStripes) has been working on exactly that challenge, in two areas where the stakes are as high as they get: healthcare and emergency response.

### **Bridging The Gap in Healthcare**

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/8a7e65be7cf2e6aa0bf01d43f6e907e2f4522c412cdef0e13e5a7daf3c6ec515.avif)

_Read our SBIR journey story:_ [_The Last Chapter of Our NSW SBIR Journey_](https://elephantstripes.ai/blog/the-last-chapter-of-our-nsw-sbir-journey)

In 2022, South Western Sydney Local Health District (SWSLHD) issued a challenge through the NSW Small Business Innovation and Research (SBIR) program. The problem was stark: around 40% of SWSLHD’s community speaks a language other than English at home. When a professional interpreter is unavailable, consultations proceed anyway. Information gets lost. Patients leave without fully understanding their condition.

VideoTranslatorAI took on that challenge. Through Phase 1 feasibility study and then the Proof of Concept phase, the team built MediCALD, a production-ready AI platform designed to support real-time multilingual conversations between patients and healthcare workers. The project was completed in 2026.

### **Keeping Communities Safe When It Matters Most**

![NHTP](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/5bd7b2b3718a15435135a21508a20ab40774cae6c63d0d3c78e5c25f0547f103.avif)

_Read our NHTP journey story:_ [_Wrapping Up the NHTP Project: Partners, Progress, and New Feature_](https://elephantstripes.ai/blog/wrapping-up-the-nhtp-project-partners-progress-and-new-feature)

On the emergency response side, VideoTranslatorAI was selected under the NSW Natural Hazards Technology Program (NHTP) to develop multilingual emergency alert solutions for culturally and linguistically diverse (CALD) communities across New South Wales.

The collaboration brought together four NSW Government agencies: the Office of the Customer Service and Digital Government (OCSE), the Department of Customer Service (DCS), NSW State Emergency Service (SES), and NSW Rural Fire Service (RFS). The goal was straightforward: when a hazard warning goes out, every community needs to understand it, regardless of the language they speak at home.

In September 2024, at the AFAC24 Conference, NSW Minister Dib publicly recognised VideoTranslatorAI’s technology for its role in enhancing public safety. That recognition, from the Minister responsible for both emergency services and digital government, reflected the seriousness with which the NSW Government has taken the multilingual communication problem.

@[youtube](https://www.youtube.com/watch?v=liggBa25X2g)

## **The Human Thread That Runs Through All of It**

It would be easy to read a story like this and conclude that AI is taking over from human translators. That reading misses the point entirely.

St. Jerome did not just convert words. He made a choice, thousands of choices, about meaning, nuance, and what mattered most to convey. That interpretive intelligence, the ability to understand not just language but context, culture, and consequence, remains irreplacably human.

What AI offers is reach. The ability to serve a language combination that no interpreter is available for. The ability to operate at 2 am when no agency is reachable. The ability to scale across thousands of simultaneous conversations in a hospital network or an emergency broadcast.

The best outcome is not AI instead of human translators. It is human translators doing the high-stakes, high-nuance work they are trained for, while technology carries the load in the gaps.

## **To Every Translator and Interpreter**

![Photo by Wilhelm Gunkel on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*0_d-lefsCQKWSweG "Photo by Wilhelm Gunkel on Unsplash")

On International Translation Day, we want to say something plainly: **the work you do is remarkable.**

You carry not just words but intent, tone, culture, and context from one world into another. You make it possible for a doctor and a patient to trust each other. For a business to operate across borders. For a community to feel seen and heard in their own language.

The world you have built across 1,600 years of this work is a more connected, more comprehensible, and ultimately more human place.

Thank you.

* * *

_VideoTranslatorAI builds AI-powered live interpretation tools for multilingual meetings, healthcare, and emergency communications. Recognised under two NSW Government programs, with 18 published milestones across 8 years. Ready to break down language barriers in your organisation?_ [**_Sign up today_**](https://app.videotranslator.ai/welcome) _and get 15 free minutes of live interpretation. No credit card required._

1,600 Years of Translation: What Changed Today

Celebrating International Translation Day 2026

Celebrating International Translation Day 2026. International Translation Day falls on 30 September, St Jerome's feast day, to honour that legacy. The United Nations formalised the observance in 2017...

At MetLife Stadium in New Jersey, a Mexican journalist raised his hand.

Rodrigo Ornelas, from TV Azteca, wanted to ask Achraf Hakimi a question. In Spanish. Hakimi, born and raised in Madrid, is a fluent Spanish speaker. He understood the question perfectly. Anyone in the room could see that.

But a FIFA official cut Ornelas off. Spanish wasn’t permitted at that press conference.

What happened next was telling. Hakimi didn’t comply quietly. He pushed back, visibly trying to make the exchange happen anyway. The moment was [caught on camera](https://x.com/AllFutbolMX/status/2065860292462919826?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2065860292462919826%7Ctwgr%5E70519fe0f58d574a1ea8bd87cc0ebe7ae544e594%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fawfulannouncing.com%2Fsoccer%2Ffifa-world-cup-press-conference-translation-only-spanish.html), shared widely, and within hours the FIFA Spanish language ban was dominating sports media across three continents.

@[youtube](https://www.youtube.com/watch?v=tU9g1LRZOfg)

FIFA reversed the rule on 15 June, announcing a Spanish interpreter would be provided. But by then, the story had already taken on a life of its own.

## **What Actually Caused the Ban?**

![Photo by Markus Winkler on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*eK495cm3YxXzej3r "Photo by Markus Winkler on Unsplash")

This is more interesting than the headlines suggested.

FIFA didn’t draft a policy saying “**no Spanish allowed**.” There was no meeting where someone decided to exclude the world’s second most spoken language from a global football tournament.

What happened was more mundane, and in some ways more troubling. FIFA’s press conference system was built to provide interpretation only for the languages of the competing teams at each session.

In a match between Morocco and Brazil, that meant Arabic, Portuguese and French. Spanish, despite being spoken natively by more than 500 million people worldwide, wasn’t in scope.

The same gap surfaced at Vinicius Jr.’s press conferences. It affected sessions with Frenkie de Jong. These weren’t isolated incidents. They were symptoms of a system that hadn’t been designed to handle the full linguistic reality of a tournament being co-hosted by a Spanish-speaking country.

No one sat down and decided to exclude Spanish speakers. They just didn’t think to include them.

That distinction matters because it tells us where the failure lives.

It wasn’t a policy decision. It was a **planning gap**, the kind that happens when language logistics get treated as a secondary concern rather than a fundamental one.

## **The Blowback was Never Really about Logistics**

When the video of Ornelas and Hakimi spread across social media, the reaction wasn’t a measured “FIFA made an operational error.” It hit deeper than that.

Spanish is the first language of seven World Cup-qualified nations in 2026. It is spoken by more than 500 million people natively and by close to 600 million in total. Mexico, one of the three co-hosts of this tournament, is a Spanish-speaking country. Matches are being played on Mexican soil.

And yet a Spanish-speaking journalist, at a World Cup press conference, was told he couldn’t ask his question.

The perception, regardless of intent, was one of cultural dismissal. Social media moved fast. “FIFA bans Spanish” was trending before most people had read past the headline. The story had already calcified into something larger than a logistical oversight. It became a symbol of whose voices get centred at global events, and whose don’t.

That’s a hard story to correct with a policy update, even a swift one.

FIFA’s reversal helped, and it should be acknowledged that they moved relatively quickly once the video spread.

![Some comments under the viral Hakimi and Orleans interview clip](https://miro.medium.com/v2/resize:fit:1400/1*VzRB1ciaV_pUb_xyXmfuHg.png "Some comments under the viral Hakimi and Orleans interview clip")

(Source: [@AllFutbolMX on X](https://x.com/AllFutbolMX/status/2065860292462919826)).

But the response was reactive. The institution was found flat-footed, not because of malice, but because of something arguably worse from a brand perspective:

> _a failure to anticipate something entirely foreseeable._
> 
> ## **Why Does This Keep Happening?**
> 
> ![Photo by Johannes Hübner on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*dVe6wLxB6bEbq9IJ "Photo by Johannes Hübner on Unsplash")
> 
> This is the part that rarely gets discussed honestly: **_language logistics at major sporting events are genuinely difficult to get right._**
> 
> Not impossible. Difficult.
> 
> The 2026 World Cup spans three countries, 16 host cities, and 48 national teams communicating in more than a dozen primary languages. On any given day, press conferences might cycle through Korean, Arabic, Dutch, Swahili, Portuguese, French and Spanish.
> 
> The traditional model of sourcing, credentialing, and deploying human interpreters at that scale requires months of advance planning, a reliable pool of accredited professionals and near-perfect operational coordination.
> 
> Most organising committees are stretched across thousands of competing priorities. Language services, invisible when they work and catastrophic when they don’t, tend to get planned late and provisioned inadequately.
> 
> This pattern isn’t new. The 2022 World Cup in Qatar had significant translation friction in the media zones. The Tokyo Olympics saw athletes, journalists and officials navigate real-time bottlenecks that slowed communication and frustrated broadcasters. UEFA has fielded press access complaints about interpreter availability for years.
> 
> The underlying issue is structural underinvestment in something that looks like a solved problem right up until the camera is rolling, and it isn’t. Tournament organisers assume the problem is smaller than it is. They plan for the languages they expect. They underprepare for the ones they don’t.
> 
> And then a journalist raises their hand.

## **The Real Cost is Trust**

![Photo by Victoria Prymak on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*F5tn0ozWJLeVgP3D "Photo by Victoria Prymak on Unsplash")

There’s a version of this story that ends with “FIFA fixed it.” They reversed the policy, added an interpreter, and we moved on.

But the more instructive version asks: **_what did this moment reveal about how major institutions think about language access?_**

When Rodrigo Ornelas was stopped from asking his question, he wasn’t just inconvenienced. He was made to feel, in front of a watching media scrum, that his language didn’t belong in that room. That his audience, and by extension his culture, was an afterthought at an event his country is co-hosting.

That feeling doesn’t disappear when the interpreter arrives.

Multicultural audiences are paying attention at a level they weren’t a decade ago. Sponsors are watching the social media response in real time. Broadcasters in Spanish-speaking markets noticed.

A tournament positioning itself as a celebration of global football cannot afford to deliver the message, even accidentally, that 500 million Spanish speakers are an afterthought.

Language access is a form of respect. When it breaks down publicly, the reputational cost is disproportionate to the operational failure that caused it.

The gap between “**_we made a logistics mistake_**” and “**_we excluded your language_**” is enormous in perception, even when the intent was never exclusion.

## **There is a Better Answer Emerging**

The good news is that the infrastructure problem FIFA ran into doesn’t require a small army of human interpreters to solve.

AI-powered real-time translation has matured considerably in recent years, and it’s increasingly being built for the exact environments where these failures occur: high-volume multilingual press conferences, broadcast sessions, live interviews, and international events where communication needs to flow across languages without delay or a last-minute scramble.

Tools like [videotranslator.ai](http://videotranslator.ai), built by [Elephant Stripes](https://elephantstripes.ai/), are designed for this kind of environment. The platform covers in-person interpretation, video calls, and broadcast scenarios. Its Broadcast mode specifically supports one-to-many multilingual interpretation: a single live session, multiple language outputs, delivered in real time.

![How VideoTranslatorAI’s broadcast works](https://miro.medium.com/v2/resize:fit:1400/1*Le_gAXz4U8t_cGTfGP-NsQ.png "How VideoTranslatorAI’s broadcast works")

That’s the exact configuration FIFA’s press conferences needed and didn’t have.

It won’t replace the human element for every high-stakes interaction. Nuance, cultural context, and diplomatic sensitivity still matter enormously.

But AI translation offers something that a last-minute interpreter scramble simply can’t: scalability, consistency, and the ability to cover languages the event planners didn’t anticipate needing until the hand goes up and the camera is rolling.

For tournament organisers, the case is straightforward. The technology exists. The cost of not using it is demonstrably higher than the cost of deploying it.

## **The Bigger Lesson**

![Photo by Fauzan Saari on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*hY8kXBQK7vR5Bf9g "Photo by Fauzan Saari on Unsplash")

FIFA’s Spanish ban was, ultimately, a logistics failure that became a cultural flashpoint.

The lesson isn’t that FIFA is culturally dismissive, or that global sporting bodies are beyond reform. The lesson is simpler and more applicable across every large-scale international event: language access needs to be treated as critical infrastructure, not a secondary concern that gets sorted out when everything else is in place.

Build it in early. Plan for the languages you didn’t anticipate. Use available technology to cover the gaps that human resources alone can’t fill at scale.

The camera is always rolling somewhere. A journalist always has their hand up. The question is whether the system is ready to let them ask it in their language.

And right now, more often than it should be, the answer is no.

* * *

[**_VideoTranslatorAI_**](https://videotranslator.ai/) _offers real-time transcription, translation, and customisable summary for meetings in_ [_50+ languages_](https://videotranslator.ai/product-overview/available-languages/)_._

_Struggling with language barriers in daily conversations or meetings? Drop a message_ [_here_](https://videotranslator.ai/contact-us)_!_

The language ban that exposed a much bigger problem.

FIFA Bans Spanish, Then Backtracks Fast

The language ban that exposed a much bigger problem. Ornelas, from TV Azteca, wanted to ask Achraf Hakimi a question in Spanish. But a FIFA official cut Ornelas off

Last week, I wrote an article called “[_A Translator Called Me Out, and They Were Right._](https://medium.com/@tathagat.banerjee/a-translator-called-me-out-they-were-right-2a61934bbb92)” I expected some disagreement. I expected the usual pushback about AI being inevitable, about adaptation being necessary, about how technology always creates more than it destroys.

What I did not expect was the volume and consistency of what came back.

Professional translators across LinkedIn responded with the same core message: you still do not fully understand how bad this has become.

Honestly, I think they are right about that, too.

## **The Feedback Was Not Abstract**

![](https://miro.medium.com/v2/resize:fit:1400/1*xDpO7X_ZOBze51uggkbaMw.png)

The responses were not theoretical concerns about some distant AI future. They were people describing a profession being economically hollowed out right now, in real time, in measurable ways.

I think the AI industry needs to sit with that, rather than immediately pivoting to optimism, which appears to be our default.

One comment reframed the conversation in a way I keep coming back to. She said: we need to stop asking “**will there be translators in the future**” and start asking “**will it be sustainable to be a translator in the future.**”

That is an entirely different question. And it is the right one.

Most AI discussions still frame everything around replacement, as in, will AI replace translators? But the people actually living through this are talking about sustainability.

Can you still earn a middle-class income doing this work? Can you still build a career around it? Can you justify the years spent mastering languages, culture, specialisation, nuance, domain expertise?

These are economic questions. Not technological ones. And the AI industry often avoids them because they are much harder to answer.

## **The Productivity Trap**

![Photo by Andreas Klassen on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*kKXKqKGmhy63DPFW "Photo by Andreas Klassen on Unsplash")

One of the most repeated ideas in AI is that AI handles the repetitive work so humans can focus on higher-order work.

You hear this constantly. “**Let the AI do the simple stuff. You do the complex stuff. Unlock creativity. Add strategic value.”**

The translators who responded pointed out the gap in that argument that I had glossed over. The industry never explains what the compensation model for that higher-order work actually looks like.

Because here is what is actually happening in post-editing work, which is the role many translators have been pushed toward: You are no longer translating from scratch. You are reviewing AI output. Monitoring for subtle errors, hidden inaccuracies, awkward phrasing, cultural mismatches, false fluency. The market response to this has largely been: you touched fewer words, therefore we pay you less.

There is a logic to that from a buyer’s perspective. But from the worker’s perspective, the work has not actually become easier. In many cases it has become more mentally draining. You are not reading your own prose for errors. You are reading AI output that often looks correct on the surface while containing mistakes that require genuine expertise to identify.

One commenter described it precisely: “**You are constantly monitoring for things that look right but are not. That is cognitively exhausting in a different way from translation itself**.”

The result is a profession that has become harder while simultaneously becoming less economically valuable. The efficiency gains do not flow to the translator. They flow upwards, to buyers, to platforms, to whoever controls distribution. The translator absorbs the cognitive load and hands the savings to the client.

That combination is not sustainable. And the translators are saying so clearly.

## **The Cruelty of the Transition**

![Photo by Vitaly Gariev on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*SWnlfNSdJtVfhmpe "Photo by Vitaly Gariev on Unsplash")

Several responses touched on something I think deserves to be named directly.

Some commenters used the word “**cruel**” to describe the specific emotional dynamic of the current transition. I think that word is accurate, and I want to explain why.

These translators are not anti-technology. They are not refusing to adapt. They are not Luddites. Many have used computer-assisted translation tools like TRADOS for years. They adopted translation memory systems, terminology databases, workflow automation. They welcomed tools that made them better at their work.

The argument is not that technology is bad. The argument is that the economic value of their labour is collapsing faster than new forms of value are emerging.

And what makes it cruel is the contradiction they are being asked to hold simultaneously. The industry tells them: your expertise matters, human nuance still matters, cultural understanding still matters. And while that message is being delivered, their rates are collapsing in real time.

Being told your skills are irreplaceable while watching your income decline is a psychologically specific form of damage. It is not the same as being told you are redundant. It is being told you are valuable while being paid as if you are not.

## **The Training Problem Nobody Wants to Say Plainly**

Several translators raised something that the AI industry still does not particularly like discussing openly.

Their work is being used to train the systems that are replacing them.

One commenter put it in a way that has stayed with me: “**every edit, every correction, every quality adjustment is being squeezed for intelligence and fed back into the engines.”**

![](https://miro.medium.com/v2/resize:fit:1400/1*snGg9VeUTbOXY9YB7qHI_Q.png)

That sentence is uncomfortable. It points to something structurally different about AI compared to earlier tools.

Historically, tools amplified labour. A word processor made you faster at writing. A CAT tool made you faster at translating. The tool served the worker.

AI increasingly **_absorbs labour_**. Every correction a translator makes to an AI output potentially improves the model. Every high-quality translation they produce potentially reduces the future dependence on translators who produce that kind of quality. The worker trains the system that is competing against them.

Whether you think this is inevitable, good, bad, necessary, or some complicated mix of all of those things, it fundamentally changes the relationship workers have with the technology. Using the tool can feel indistinguishable from participating in your own displacement.

That is a very different psychological contract than any previous generation of workers has been asked to accept.

## **The Bigger Economic Question**

@[youtube](https://www.youtube.com/watch?v=bJ22BrRh3jg)

Several comments across platforms raised a question that sounds dramatic until you sit with it.

> “If AI keeps reducing the value of human labour across industries, who actually has money left to participate in the economy?”

Translation, writing, design, programming, support work, research, media, and coordination tasks. The jobs being impacted are predominantly white-collar knowledge work roles.

The core tension is this. AI massively increases productive capacity. But capitalism still distributes income primarily through labour. So what happens when society becomes more productive while human work becomes less economically valuable?

This is not a translation industry question. It is an economic question that affects many industries, and I do not think we have great answers to it yet. But it is an important question, and translators are asking it from the front line.

## **The Canary is Already in the Mine**

![Photo by Eddie Lau on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Jk7TTwnWoBpk5FyU "Photo by Eddie Lau on Unsplash")

One reason translators deserve particular attention right now is that they are simply earlier than most knowledge professions at the bleeding edge of this transition.

Translation is one of the first knowledge professions to experience full-scale AI commodification. The outputs are digital. The evaluation criteria are probabilistic. The work maps extremely well onto what large language models are structurally good at. That is why the disruption hit translation so hard and so fast.

Translators are effectively living through a future that many other professions are only beginning to glimpse.

What they are telling us is this: the technological transition is moving much faster than the economic transition. The tools arrived. The compensation models have not. The rights frameworks have not. The labour protections have not. The new ownership structures have not. And the people caught in the middle are absorbing the shock personally.

## **Two Things Can Be True at Once**

I want to be clear about where I stand, because this matters given that I build AI tools, including [VideoTranslatorAI](https://videotranslator.ai/).

I still believe AI translation creates enormous positive value. VideoTranslatorAI started as an interpretation tool for clinicians when a professional interpreter was not available. That gap was real. The access it provides to patients who would otherwise have had no interpretation at all is genuinely good. I believe that multilingual communication becoming more accessible is broadly positive for humanity.

But I also think it is entirely possible for technology to create real societal value while simultaneously causing genuine economic pain to specific groups of people. Both can be true at the same time.

The translators who responded to my article deserve credit for forcing that contradiction into the open. For refusing to let the AI conversation stay comfortably abstract. For saying plainly that while the rest of the industry is discussing productivity, they are discussing survival.

Those are not the same conversation.

* * *

_Thank you to every translator who took the time to respond. The quality of your feedback changed how I think about this, and that is the point of writing publicly about hard things._

_Their valuable thoughts are available under the LinkedIn post I posted this week. If you’re interested in reading the whole discussion, click_ [_here_](https://www.linkedin.com/feed/update/urn:li:activity:7463460480326488064/)_._

What translators are experiencing is a preview of where this is all heading

Translators vs. AI Reality

What translators are experiencing is a preview of where this is all heading Last week, I wrote an article called “A Translator Called Me Out, and They Were Right.”

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://app.videotranslator.ai/welcome) 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://app.videotranslator.ai/welcome)

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://app.videotranslator.ai/welcome)

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://app.videotranslator.ai/welcome)

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

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

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._

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

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