Today is International Labour Day.

A day that started as a tribute to workers who fought for basic dignity, fair hours, and safe conditions.

A day that in 2026 carries a particular weight, because a lot of people are heading into the long weekend quietly worried about whether there will still be a job waiting for them when the world catches its next breath.

That worry deserves to be taken seriously, not dismissed with optimistic hand-waving about new opportunities and inevitable progress.

So let’s be honest about what is actually happening, because the full picture is more complicated, and ultimately more hopeful, than most of the current discourse suggests.

## **The Collective Action Problem Nobody Wants to Name**

El Salvador’s President [Nayib Bukele](https://x.com/nayibbukele/status/2043185432972722445) posted something on X last month that cut through a lot of noise.

![Source: X](/api/media/elephantstripes-prod/cms/originals/blog-inline/ab2d15836626eb840fd7e00ec900425f97115ccc4838ea582c3ed4569dca9dd1.avif "Source: X")

That is an unusually clear-eyed observation from a sitting head of government, and it names the structural risk that most corporate communication carefully avoids. Individual companies are making rational decisions. The aggregate result of those decisions is potentially irrational at the system level.

![Challenger Report Annual Job Cut Announcements 2015–2025 (Source: Challenger, Gray & Christmas, 2026).](https://miro.medium.com/v2/resize:fit:1400/0*iI-q68S-uEVSEEgC.png "Challenger Report Annual Job Cut Announcements 2015–2025 (Source: Challenger, Gray & Christmas, 2026).")

[Challenger, Gray & Christmas](https://www.challengergray.com/blog/2025-year-end-challenger-report-highest-q4-layoffs-since-2008-lowest-ytd-hiring-since-2010/) reported that AI was blamed for 50,000 layoffs in 2025. The tech sector is the most visible example, but the pattern is spreading into marketing, legal support, customer operations, and financial services. Real people are losing real jobs, and the downstream effects on spending, confidence, and community tax bases are real too.

Sam Altman has said publicly that some companies are using AI as a convenient scapegoat for workforce decisions that had more to do with pandemic over-hiring and poor financial management than genuine automation.

There is some truth to that. Corrections have been underway since 2022, and most have not been associated with AI adoption, which really began to affect coding in 2023 and 2024. In other words, the layoffs began for financial reasons.

But that framing is also a convenient deflection. **The jobs are still gone**. The scapegoat versus genuine cause distinction matters less to the person updating their resume at midnight than it does in a policy debate.

## **The “Tech Hiring Is Dead” Narrative**

@[youtube](https://www.youtube.com/watch?v=0j1HqEEDThc)

Dario Amodei at Anthropic has made statements about AI wiping out white-collar jobs and coding roles within the next few years. He said it plainly, and the comments rippled through the tech community in ways that reinforced a growing narrative: that a career in software development is now a bad bet.

I do not think that holds up against the actual data.

The [Indeed AI Tracker](https://www.hiringlab.org/2026/01/22/january-labor-market-update-jobs-mentioning-ai-are-growing-amid-broader-hiring-weakness/) reached a high of 4.2% at the end of 2025. Job postings mentioning AI have grown consistently since mid-2023. Employer job postings related to AI jumped 117% between 2024 and 2025.

LinkedIn data tells the same story. AI has already added more than [1.3 million new roles](https://www.weforum.org/stories/2026/01/ai-has-already-added-1-3-million-new-jobs-according-to-linkedin-data/?gad_source=1&gad_campaignid=22228224717&gbraid=0AAAAAoVy5F4JlvyK4rUqmaS-YvSZPOeID&gclid=CjwKCAjw-8vPBhBbEiwAoA39Woe6mQZX1_buFGUtI9YbtRr40-az4skZCiUwUA81om3E-BfNLAXS9RoCNTEQAvD_BwE), including AI Engineers, Forward-Deployed Engineers, and Data Annotators. More than 600,000 new AI-enabled data centre jobs have been created.

[The US Bureau of Labour Statistics](https://www.bls.gov/ooh/computer-and-information-technology/) projects computer and information technology occupations to grow at nearly three times the average rate for all jobs through 2034. Professionals with AI expertise earn 56% more on average than peers without it.

This is not a hiring market where tech is dead. It is a hiring market where the skills being rewarded have shifted dramatically, and where people who have not made that shift yet are experiencing real pain.

The distinction matters. “Tech hiring is dead” is a counsel of despair that leads people to exit a field where opportunity is actually growing. “Tech hiring has shifted and requires different skills” is uncomfortable but actionable.

For workers, developing and highlighting relevant AI skills may be the key to landing a job in 2026, particularly in occupations with otherwise muted hiring activity. That is not cheerful messaging. But it is accurate, and accurate is more useful than comfortable.

## **What the Broader Market Actually Looks Like**

![Photo by Srinivasan Venkataraman on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Dm6u-bhVbOOpEsiB "Photo by Srinivasan Venkataraman on Unsplash")

The overall picture is one of genuine contradiction, which is why the conversation is so difficult.

Companies are laying off staff, insisting AI will “do more with less,” yet they haven’t found ways to deploy AI at scale. Recruiters say entry-level pathways are narrowing, but critical roles remain hard to fill.

Just over half of people say they’re job hunting in 2026, while nearly 80% feel unprepared to find a new job. That gap between the number of people searching and the number who feel equipped is where a lot of the anxiety is living.

Recent college graduates are bearing a disproportionate share of the disruption. Those aged 22 to 27 saw an unemployment rate of 5.6% at the end of last year, compared to the overall rate of 4.2% (The Federal Reserve Bank of New York, [2026](https://www.newyorkfed.org/research/college-labor-market#--:explore:unemployment)). Many of these graduates were headed into entry-level roles at law firms, research organisations, tech companies, and public sector agencies, precisely the environments now adopting AI to handle the tasks that basic training prepared those graduates to perform.

[The World Economic Forum](https://www.weforum.org/press/2025/01/future-of-jobs-report-2025-78-million-new-job-opportunities-by-2030-but-urgent-upskilling-needed-to-prepare-workforces/) estimates that AI could eliminate 92 million jobs by 2030 while creating 170 million new ones, a net gain of 78 million. That number is meaningless to someone who lost their job in accounting in 2025 and cannot afford to retrain for an AI engineering role by 2030. The aggregate optimism and the individual pain are both real at the same time.

## **Why This Prompted Us to Build Something**

At [Elephant Stripes](https://elephantstripes.ai/), my team and I have been watching this closely. Not from the outside, but as people who care about the practical reality of what workers are navigating right now.

The job seeker’s experience in 2026 is genuinely difficult in a specific way. The advice is abundant. The tools are everywhere. The templates are free and ubiquitous. And yet most people still have resumes that do not actually represent what they are capable of.

The standard AI resume approach makes this worse, not better. You paste your existing resume into a language model, it polishes the language and adds keywords, and you end up with a shinier version of the same document. Generic. Template-heavy. Optimised for an ATS screening tool that has seen ten thousand copies of the same format.

![](https://miro.medium.com/v2/resize:fit:1400/1*yqQR-REINmH1IeX8fFTUlQ.png)

That is why we built [**JobsLobster**](https://jobslobster.com/) differently.

JobsLobster does not just rearrange what you already have. It identifies what is missing. It asks you targeted questions about your actual skills, your specific contributions, and the outcomes you produced. And it builds something stronger from your answers, rather than from the limited version of yourself that ends up in a resume written in haste.

The differentiator is in the input. Most resume tools start with your resume. JobsLobster starts with you.

## **A Note on What Comes Next**

The transition happening right now is real and it is not finished. The disruption to entry-level work is real. The anxiety is legitimate. The companies using AI as convenient cover for decisions they made for other reasons are being less than honest about it.

And the future is not one in which technology simply eliminates work. Deloitte found that while 30% of organisations are exploring agentic AI options and 38% are piloting solutions, only 14% have solutions ready to deploy, and a mere 11% are actively using these systems in production. The wave is coming, but it is not a wall that hit overnight.

The workers who will navigate this best are the ones who are building AI fluency now, not as a panic response to headlines but as a steady, deliberate investment in the skills that the next decade of work will reward.

That is not naive optimism. It is what the data actually says.

Happy Labour Day. The work is changing. So are the people doing it.

\---

_Are you interested in getting early access to JobsLobster? Drop us a message_ [_here_](https://elephantstripes.ai/contact) _to stay updated!_

Labour Day in the Age of AI Layoffs - Tat Banerjeee

Labour Day in the Age of AI Layoffs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

## **Where This Goes From Here**

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

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

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

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

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

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

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

* * *

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

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

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

It’s the summer of 2016.

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

You are not sightseeing. You are catching a Snorlax.

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

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

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

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

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

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

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

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

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

That sentence deserves a moment.

![CEO of Niantic Spatial, John Hanke (Source: BBC)](https://cdn-images-1.medium.com/max/1440/0*syaDmN_pWKj2fffK.jpg "CEO of Niantic Spatial, John Hanke (Source: BBC)")

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

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

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

## **Why GPS Alone Is Not Enough**

![Image by ALAMY (Source: BBC)](https://cdn-images-1.medium.com/max/1440/0*vAE4DhqGIagwsIi5.jpg "Image by ALAMY (Source: BBC)")

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

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

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

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

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

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

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

That richness is what makes the dataset genuinely irreplaceable.

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

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

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

Hanke calls it a _“living map”_.

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

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

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

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

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

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

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

This is where it gets spicy, and fair enough.

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

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

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

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

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

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

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

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

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

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

That’s it. Not complicated. Just honest.

## **Gotcha, Capitalism!**

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

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

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

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

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

Gotcha, capitalism.

* * *

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

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

You Trained Delivery Robots While Playing Pokémon Go. Unknowingly.

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

A recent [NBC News poll](https://www.nbcnews.com/politics/politics-news/poll-majority-voters-say-risks-ai-outweigh-benefits-rcna262196) surveyed 1,000 registered American voters and asked them to rate a list of institutions, policies, and technologies.

AI came in near the bottom: 26% positive, 46% negative. Only Democrats and Iran ranked lower.

ICE, the immigration enforcement agency that has dominated headlines for its controversial deportation raids, polled better than artificial intelligence.

Let that sink in.

Here’s the kicker: 56% of those same respondents had used an AI platform like ChatGPT or Copilot in the previous month. These are not people who have never touched the technology. They use it. They just don’t trust it, or the people behind it.

That is not a public education problem. That is a communications disaster, and the architects of that disaster are the very leaders who built the

![How voters feel about political figures and topics (Source: NBC News)](https://cdn-images-1.medium.com/max/1440/1*pyQOYTJ-fmWCAVSDpTLRmA.png "How voters feel about political figures and topics (Source: NBC News)")

## **The Fear Factory**

Cast your mind back to 2022 and 2023. The dominant narrative from AI’s most prominent voices was existential dread.

[Sam Altman](https://edition.cnn.com/2023/10/31/tech/sam-altman-ai-risk-taker) warned Congress that AI might destroy civilisation. [Geoffrey Hinton](https://www.nytimes.com/2023/05/01/technology/ai-google-chatbot-engineer-quits-hinton.html), the so-called “Godfather of AI,” resigned from Google and spent months telling journalists that the technology he helped build might end humanity. [Elon Musk](https://www.techpolicy.press/chatgpt-safety-in-artificial-intelligence-and-elon-musk/), who co-founded OpenAI before departing, called AI “more dangerous than nukes.”

![OpenAI CEO Sam Altman speaks before a Senate Judiciary Subcommittee on Privacy, Technology and the Law hearing on artificial intelligence, on May 16, on Capitol Hill in Washington (Photo by Patrick Semansky/AP)](https://cdn-images-1.medium.com/max/1440/0*qidLKuNvit4eWRly "OpenAI CEO Sam Altman speaks before a Senate Judiciary Subcommittee on Privacy, Technology and the Law hearing on artificial intelligence, on May 16, on Capitol Hill in Washington (Photo by Patrick Semansky/AP)")

Nvidia’s [Jensen Huang](https://www.inc.com/leila-sheridan/jensen-huang-has-had-it-with-your-ai-slander/91287603) has since publicly acknowledged that AI doomerism accounts for “90% of the messaging” around the industry. He called it **_“extremely hurtful”_** and said it has “**_done a lot of damage.”_** In a January 2026 interview, he expressed frustration that fear narratives were deterring people from embracing the technology.

But here is the question Huang’s frustration conveniently sidesteps: 

> **_Where did that doomerism originate?_**

It did not spring up from a fearful public. It was seeded, cultivated, and amplified by the industry itself.

## **Fearmongering is a Business Model**

There is a well-worn pattern in how high-stakes technologies get brought to market.

First, establish urgency. Make the stakes feel enormous. Create the sense that society stands at a precipice, and that only the people in this room, with this technology, understand the magnitude of the moment.

That framing works exceptionally well when you need to raise capital.

Venture capitalist Chamath Palihapitiya has been candid about how AI hype dynamics operate. The doom narrative served a purpose during the fundraising cycle: it communicated that this technology was powerful, consequential, and world-altering. Investors needed to take it seriously. Missing the AI wave was framed as an existential risk in itself.

Then the money arrived. Billions of it.

And the messaging quietly shifted.

@[youtube](https://www.youtube.com/watch?v=wbCGaKd9_yQ)

Today, the dominant pitch from AI companies is not “we are building something that might destroy civilisation.” It is “AI is a utility, like electricity. It will make your business more efficient. Here is the pricing plan.”

Sam Altman now talks about AI integration into daily life as naturally as running water. Jensen Huang positions Nvidia’s chips as critical infrastructure. Microsoft bundles Copilot into Office 365 subscriptions as though it is no more remarkable than spell-check.

The whiplash is dizzying. And the public noticed.

When the people who spent two years warning you about AI’s civilisation-ending potential turn around and ask you to pay a monthly subscription fee for it, the reasonable response is suspicion. You do not buy electricity from someone who just told you power lines might kill everyone.

[Fortune](https://fortune.com/2026/03/04/sam-altman-jensen-huang-only-themselves-to-blame-ai-scare/) magazine put it plainly in a March 2026 commentary: “Sam Altman, Jensen Huang and the other AI kingpins only have themselves to blame for the scare rippling through the economy right now.”

## **The Most Popular Fearmongering Topic about AI**

The job displacement angle deserves particular attention because it is the one that most directly affects how young people relate to AI right now.

The dominant story is one of replacement. AI takes jobs. Specific roles become obsolete. The future is one of mass unemployment or, at best, a painful transition period where large categories of workers are left behind.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/5f223ffb618fe795e98643bdff0843757ddf4eaf819b24e0ce81070d3fe1d283.avif)

This story is not entirely fabricated. There are real disruptions happening in specific sectors, and it would be dishonest to wave them away. But the framing is wrong in a way that matters enormously for how people respond to it.

The history of transformative technology is not primarily a story of jobs disappearing. It is a story of work **changing**, and of the people who adapted to those changes gaining enormous advantages over those who did not.

The introduction of spreadsheet software did not eliminate accountants. It changed what accounting looked like, made some tasks irrelevant, created new ones, and shifted the value of the profession toward judgment and analysis rather than manual calculation. The people who resisted learning spreadsheets did not save their jobs. They just fell behind.

The question for AI is not whether your current job will look the same in ten years. It probably will not, just as the accountant’s job in 1995 does not look like the accountant’s job in 1985. The question is whether you will be the person who shapes what the new version of your field looks like, or the person who discovers that shift from outside.

AI is a tool. A powerful, genuinely remarkable tool unlike most that have come before it.

But the frame of “tool” matters. Tools do not make decisions about your career. People do.

## **The Impact on AI Literacy**

![Photo by Hugh Han on Unsplash](https://cdn-images-1.medium.com/max/1440/0*KdUDw1Ao7yYr_0hL "Photo by Hugh Han on Unsplash")

Here is what the fear narrative is actually costing us, and it is not the companies or the investors. They will be fine regardless.

The real casualty is the ordinary person who has decided, based on everything they have absorbed from the news cycle, that AI is either a threat to their job, a tool for misinformation, a surveillance mechanism, or some combination of all three.

That person is not developing **AI literacy**. They are not experimenting. They are not building the habits and the fluency that are, increasingly, becoming a foundational part of modern professional life.

Think about what it meant to be able to type with all ten fingers in the 1990s. It was not a competitive advantage in a narrow sense. It was a prerequisite. If you couldn’t type with reasonable speed and accuracy, you were simply slower at almost every white-collar task than the people who could.

The skill was not glamorous, and it was not discussed in terms of transformation or disruption. It was just something you needed to know how to do.

AI literacy is shaping up to be the same kind of thing. Not in the sci-fi sense of understanding large language model architecture or knowing how to fine-tune a model.

In the practical sense of knowing what AI tools are available, what they are genuinely good at, how to prompt them effectively, when to trust their outputs and when to verify them, and how to build habits that make your working life meaningfully more capable.

That skill is not optional. It is becoming a baseline. And every month that a person delays developing it because they have internalised a fearmongering narrative is a month of compounding disadvantage.

## **Start From Where You Are**

![Photo by DISRUPTIVO on Unsplash](https://cdn-images-1.medium.com/max/1440/0*wfztYgrr-LJjcxtA "Photo by DISRUPTIVO on Unsplash")

If you are reading this and you have been putting off engaging with AI because the whole thing feels overwhelming, or ominous, or like something designed for people more technical than you, here is a more useful frame.

You do not need to understand how it works. You need to understand **_how_** to use it. Those are very different things. Nobody who uses a GPS needs to understand satellite triangulation. Nobody who uses a word processor needs to understand how spell-check algorithms are built. The baseline skill is not technical comprehension. It is practical fluency.

Start somewhere small and concrete. Use an AI tool for a task you already do. See what it does well and what it does badly. Build from there. The gap between where you are now and where you need to be is almost certainly smaller than the fearmongering suggests, and the compounding benefits of starting sooner rather than later are almost certainly larger.

The noise around AI is not going to get quieter. The incentives that produce it are too strong and too entrenched. But you do not have to let the noise make your decisions for you.

The tool is here. It is genuinely useful. Learning to use it well is, increasingly, just part of what it means to work in the modern world.

That is a much less exciting headline than “AI will end humanity as we know it.” But it is closer to the truth.

AI Leaders Sold You Fear, Then Sold You Subscriptions

AI Leaders Sold You Fear, Then Sold You Subscriptions - Tat Banerjee

You’ve probably seen the headlines.

AI is stealing jobs. AI is making students lazy. AI is dangerous, biased, untrustworthy. Scroll through any news feed and the message is consistent:

> **Be afraid, be very afraid.**

So when a Sydney entrepreneur named Paul Conyngham quietly used ChatGPT, AlphaFold, and Grok to help design the world’s first personalised cancer vaccine for his dying rescue dog, Rosie, you might have missed it.

It didn’t have the same dramatic energy as the fear-based stories. It was just a man, a dog, a problem, and a decision to try.

Rosie had advanced mast cell cancer. Traditional treatment slowed the disease but couldn’t stop it. Conyngham, a data scientist and machine learning engineer with 17 years of experience and no medical training, spent $3,000 sequencing her DNA at UNSW’s Ramaciotti Centre for Genomics.

Conyngham used AI tools to analyse the results, identify potential treatment pathways, and connect with Australian scientists who could help him go further.

Most of Rosie’s tumours have since shrunk. She’s back to chasing rabbits.

This is the story we should be telling young people about AI.

## **The Narrative Gap**

![Photo by Vasilis Caravitis on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*FaDjdCXNcrUe8Jlr "Photo by Vasilis Caravitis on Unsplash")

Here’s the problem with how we’re raising the next generation around technology:

We hand them devices, warn them about dangers, and leave them to figure out the rest. The education system is scrambling to respond to AI. Parents and teachers are often working with outdated mental models. And the loudest public voices tend to be either uncritical boosters or catastrophists.

Young people are caught in the middle, often anxious about a technology they use every day but don’t feel equipped to understand. The dominant narrative is a _loss_ narrative.

Jobs disappearing. Industries disrupted. Entire skill sets made obsolete overnight. The question most often asked is not **_“what will you build with this?”_** but **_“how do you protect yourself from it?”_**

That framing is not entirely wrong. Disruption is real, and dismissing people’s legitimate concerns about their livelihoods does not help anyone. But it is profoundly incomplete, and for young people who are still figuring out what kind of future they want to build, it is actively misleading.

Because here is what the fear narrative misses:

> **AI does not only take. It also gives.**  
> **It gives access. It gives capability.**

It gives people without institutional resources the ability to do things that previously required a lab, a team, a budget, and years of formal training.

Paul Conyngham is not a medical researcher. He is an engineer who used AI to learn enough, fast enough, to ask the right questions, find the right people, and build something that had never existed before.

That is not a threat to anyone. That is exactly what the technology is supposed to do.

## **Curiosity as a Qualification**

![Paul Conyngham and his beloved dog, Rosie (Source: X)](https://miro.medium.com/v2/resize:fit:1400/0*yCgCBqRfXPXArbe4 "Paul Conyngham and his beloved dog, Rosie (Source: X)")

One of the most underappreciated things about the Rosie story is not the vaccine itself. It is the path that led to it.

Conyngham did not sit with ChatGPT and have it design a cancer vaccine from scratch. What happened was that AI gave him **a** **map**.

The AI suggested immunotherapy as a direction, pointed him toward UNSW’s Ramaciotti Centre for Genomics, and helped him develop a plan specific enough to take to actual researchers.

When he showed up at the university, he was not a confused outsider asking vague questions. He was someone with a framework, a direction, and enough informed context to be taken seriously. That is a genuinely new kind of access.

For young people, this story should reframe the entire question of what it means to be qualified. The old model required years of formal training before you could contribute to a field.

That model hasn’t disappeared, but it now sits alongside something genuinely new_:_ **_the ability and willingness to work with AI to compress learning, navigate expertise, and take on complex problems earlier._**

However, this doesn’t mean expertise no longer matters. It means the ceiling of what a motivated young person can do has risen significantly.

Conyngham’s collaboration with UNSW researchers is the model here. He brought curiosity and AI fluency, they brought deep scientific expertise, and together they achieved something neither could have done alone.

## **What a Responsible Use of AI Looks Like**

![](https://miro.medium.com/v2/resize:fit:1400/1*yakcpE1OcSwBvf5qjHDVHw.jpeg)

Using AI well is not just about knowing which prompts to use or which tools to stack. It is about understanding what AI can and cannot do, and knowing when to hand the work over to people who carry real accountability for the outcome.

Conyngham did not try to administer a vaccine he designed by himself. He brought his AI-guided research to qualified scientists and paid for their expertise to take it from hypothesis to treatment.

The vaccine was developed by Thordarson and his team. Rosie’s care was supervised by veterinary professionals throughout. The AI was the starting point, the bridge, and the accelerant. The humans were the ones who bore responsibility for what actually happened.

That distinction is not a limitation but a design principle for using these tools responsibly and effectively. AI is extraordinarily good at helping you explore a problem space, identify connections you would not have found on your own, and generate enough structured understanding to have a meaningful conversation with someone who knows more than you do. **It’s not a replacement for that person. It is a way to reach them.**

Young people who internalise that distinction early will be better equipped to use AI in ways that are genuinely impactful. Not because they understand every line of how the models work, but because they understand the difference between AI as a starting point and AI as a final answer.

## **A Gentle Reminder from Rosie**

Rosie is back to chasing rabbits.

That image is worth sitting with. A dog that was given months to live is now running in the yard because her owner refused to accept the boundaries of his own expertise and used AI to reach further than those boundaries.

Professor Thordarson put it plainly on X after Rosie’s results came in: this technology can democratise the process of designing cancer treatments. Not just for dogs. **_For people._**

![Source: @PalliThordarson on X ](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/655443ddb64ffc5c7358747a0240480551d60ea8c5506bc3a72b11462c655a65.avif "Source: @PalliThordarson on X ")

That happened because someone decided AI was a tool for solving problems rather than a source of problems to be afraid of.

The fear narrative will keep generating clicks. It will keep filling feeds with warnings and predictions and anxious commentary about what is being lost. Some of that is legitimate and worth reading.

But every so often, a story comes along that points in a completely different direction. A dog chasing a rabbit at the park. A tumour that shrank. A vaccine that had never existed before, built in under two months, by a community of people that AI helped connect.

That is also the story. Probably the more important one.

A Tech Founder Used AI to Save His Dog From Cancer. It Worked.

A Tech Founder Used AI to Save His Dog From Cancer. It Worked. - Tat Banerjee

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

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

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

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

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

## **What Agentmaxxing Actually Means**

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

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

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

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

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

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

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

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

## **The Fearmongering Misses the Point**

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

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

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

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

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

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

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

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

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

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

That’s what _agentmaxxing_ looks like in practice.

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

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

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

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

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

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

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

## **The Optimism is Earned**

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

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

This is not that.

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

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

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

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

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

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

* * *

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

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

Agentmaxxing Can Be Your Best Career Move This Decade

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

The support ticket was simple. A billing discrepancy is easy to fix. 

But the customer spoke **Japanese**, and the agent spoke **English**, and somewhere between the two, the conversation collapsed.

No resolution. No callback. Just a public complaint and a churned account.

Language didn’t kill the relationship. The absence of a plan for language did.

## **Language Barriers Erode Customer Trust**

![Photo by Vitaly Gariev on Unsplash](https://cdn-images-1.medium.com/max/1600/0*soQ-GnN0f5d695Dd "Photo by Vitaly Gariev on Unsplash")

Customer service is fundamentally about one thing: **making people feel understood.**

Language shapes that entirely. When customers can’t articulate what’s wrong, they feel invisible.

When agents misread the situation, they confidently solve the wrong problem.

Small miscommunications carry outsized consequences in fintech, travel, ecommerce, and education.

A misunderstood instruction can block an account. A misheard address ships a parcel to the wrong country. An unclear complaint never reaches the right team.

## **Where Language Breaks Down**

![Photo by Markus Winkler on Unsplash](https://cdn-images-1.medium.com/max/1600/0*EwIRKeCRzi45xY_N "Photo by Markus Winkler on Unsplash")

The pattern looks different depending on the industry, but the outcome is the same.

Let’s think about the following scenarios:

In **fintech**, a Korean customer tries to unfreeze a flagged transaction. They can’t explain the context in English clearly enough. The account stays frozen. At the end, they decide to switch banks. That’s not a fraud prevention win. That’s a churn event caused by a communication gap.

In **travel**, a Spanish-speaking family’s flight gets cancelled. The rebooking options are explained in fast, jargon-heavy English. They miss the window, miss the connection, and lodge a complaint that ends up on social media. In this era, we all know that a simple complaint on social media can burn your brand to the ground.

In **e-commerce**, a customer in Vietnam orders the wrong variant because the return policy wasn’t understood. The agent closes the ticket. The customer files a chargeback.

In **education**, an international student can’t follow the enrolment instructions delivered in English. They miss a deadline. They defer. Sometimes they don’t come back.

Each of these is a solvable problem. None of them needed to happen.

Most companies push the problem onto the customer.

“Use English.” “Try our FAQ.” “Submit a ticket.” None of that is support. It’s deflection.

## **After the Call Is Where Things Really Break Down**

![Photo by David Travis on Unsplash](https://cdn-images-1.medium.com/max/1600/0*21RPvXFI1DEo3iev "Photo by David Travis on Unsplash")

There’s a part of the language problem that rarely gets discussed.

The call ends. What happens next?

An agent’s memory of a fast, multilingual conversation is patchy at best. Notes are rushed. Action items get dropped. The customer rings back, explains everything again, and trusts you a little less than they did before.

The second call costs twice as much as fixing the first one properly.

## **Why Hiring Your Way Out of This Doesn’t Scale**

The obvious counter-argument is: **just** **hire bilingual agents.**

It sounds sensible until you do the maths. A bilingual agent in a major market costs significantly more to recruit, train, and retain. And they only solve one language pair. Your Spanish-speaking hire doesn’t help when a customer calls in Tamil, or Tagalog, or Hungarian.

There are over 7,000 languages in the world. Even narrowing to the top 50 by global speaker count, no support team can hire their way to full coverage.

And that’s before you factor in time zones, shift patterns, and the availability of qualified bilingual candidates in niche language combinations.

Hiring is a partial solution that works at small scale, in a small number of markets, for a small number of languages. It doesn’t hold as you grow.

The smarter path is infrastructure. Build the capability into the platform, not the headcount.

## **What Solving This Actually Looks Like**

![Multilingual video call by VideoTranslatorAI](https://cdn-images-1.medium.com/max/1600/1*KjRPE3ORGVHriZaaYPM5-g.png "Multilingual video call by VideoTranslatorAI")

[VideoTranslatorAI](https://videotranslator.ai) handles live video calls between customers and agents who speak different languages. 

Both sides speak naturally in their own language. The platform transcribes and interprets speech in **real time**, delivering subtitles and voice-over simultaneously so neither party misses a word.

There’s no pause-and-wait, no awkward silence while a translation loads. The conversation flows.

When the call wraps up, the platform generates an **AI summary** of what was discussed, what was resolved, and what needs follow-up. Agents have a clean record. Customers get clarity. Nothing falls through the gaps.

It runs the same experience from São Paulo to Seoul to Stockholm.

## **What This Does for Your Agents**

Language management is exhausting work that agents were never trained for. When you remove that load, focus returns to what they’re actually good at: listening, problem-solving, and creating positive outcomes.

Cases close faster. Fewer calls escalate. Teams operate confidently across time zones without language anxiety eating into performance.

The platform currently supports [55 languages](https://videotranslator.ai/product-overview/available-languages), including Arabic, Hindi, Spanish, Korean, and Japanese. Language coverage expands in line with where customers are.

![How VideoTranslatorAI works](https://cdn-images-1.medium.com/max/1600/1*_bkMjzw05FKGNJ1W1vD-ZQ.png "How VideoTranslatorAI works")

## **The Standard Has Already Changed**

![](https://cdn-images-1.medium.com/max/1600/1*s6S3oL_Fu1S4nXv9DYJbag.png)

Customers no longer benchmark your service against your direct competitors. They benchmark it against every great service experience they’ve ever had, across every industry, in every language.

Global support used to mean a multilingual FAQ. That’s not enough any more.

Every customer deserves a conversation, not a workaround. The tools exist. The only question is whether your business uses them.

* * *

_Handling international inquiries can be tricky, but it doesn’t have to be. If you’d like to see how_ [**_VideoTranslatorAI_**](https://videotranslator.ai/) _can help your team bridge the language gap instantly, feel free to click_ [_here_](https://agent.videotranslator.ai/) _and try it for free._

Your Support Team Speaks English. Half Your Customers Don't.

Your Support Team Speaks English. Half Your Customers Don't. - Tat Banerjee

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

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

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

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

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

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

## **What is OpenClaw?**

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

## **What China Actually Gains**

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The OpenClaw story in China is not without its complications.

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

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

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

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

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

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

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

## **Beyond The Scoreboard**

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

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

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

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

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

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

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

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

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

The Shenzhen Queue That Shows China’s Real AI Appetite

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

There is a moment most multilingual professionals know intimately.

You are sitting in a meeting room, the conversation is moving fast, and somewhere between the third and fourth exchange, you realise the other person has stopped truly listening. Not because they are disinterested. Because they are lost.

They nod. They smile. They reach for their coffee at precisely the wrong moment, hoping the gesture reads as thoughtful rather than confused. And the meeting moves on, carrying its misunderstandings quietly into whatever decision gets made at the end.

Language barriers in face-to-face settings are not a niche problem. They are one of the most universal, most quietly consequential communication failures in modern professional and community life. And for decades, the solutions have been either expensive, impractical, or simply not good enough.

That is changing. Faster than most people realise.

## **Why In-Person Is the Hardest Problem to Solve**

![Photo by Headway on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Ss18PoShlQirdZa7 "Photo by Headway on Unsplash")

It is worth pausing on why in-person translation is so much harder than its digital counterpart.

When you are communicating online, you have affordances.

A chat window. A caption toggle. A quick copy-paste into a translate tab. There is time, however brief, to compensate. The asynchronous nature of digital communication creates natural buffer zones where gaps in comprehension can be quietly filled.

**In a room, there is no buffer.** There is just the conversation, moving forward at the pace of whoever is speaking.

You cannot pause a live discussion to check what someone meant. You cannot rewind a parent-teacher interview to re-examine the sentence where you lost the thread. When a doctor is explaining a treatment plan and the patient nods without truly understanding, nobody in that room is made aware that comprehension failed. The moment just passes.

Of the 7.8 billion people on the planet, only around 1.35 billion speak English, and the majority of those are not native speakers. Every day, in clinics, classrooms, council meetings, business negotiations, and community gatherings, people sit across from one another with important things to say and inadequate tools to say them.

The consequence is rarely dramatic. It is usually quiet. A missed nuance. A polite nod that masks confusion. A decision made on incomplete information. A relationship that never quite formed.

## **The Human Interpreter Paradox**

![](https://miro.medium.com/v2/resize:fit:1400/1*yjtiXsecT_q1YCl1idXv7g.jpeg)

Professional human interpreters remain the gold standard for high-stakes multilingual communication.

In a courtroom, a complex medical consultation, or a diplomatic negotiation, an experienced interpreter is irreplaceable. They carry not just language but cultural context, emotional register, and the judgement to know when something requires more than a direct translation.

Nobody serious is arguing otherwise.

But the reality of daily multilingual life does not look like a United Nations chamber.

It looks like a small business owner trying to close a deal with an overseas client who flew in unexpectedly. It looks like a community health worker trying to explain vaccination information to a newly arrived family. It looks like a school counsellor trying to have a meaningful conversation with a parent whose English extends only as far as pleasantries.

In these moments, booking a credentialed interpreter is not always feasible, financially or logistically. Interpreters cost money. They require advance notice. They need to physically travel to a location, or at minimum be available at the right time. For spontaneous, everyday, or lower-budget interactions, the interpreter model simply does not scale.

This is the gap that AI-powered translation tools are now stepping into. Not to replace human interpreters in the settings where they are essential, but to serve the vast, underserved middle ground where the alternative to an AI tool is not a human interpreter. It is no support at all.

## **What Has Actually Changed**

![Photo by Alex Knight on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*GDZr0BTgniLMZ7D_ "Photo by Alex Knight on Unsplash")

For a long time, AI translation tools were impressive in controlled environments and frustrating everywhere else.

They stumbled on accents. They flattened idiom. They introduced a mechanical, halting quality to speech that made conversations feel like they were being filtered through a badly subtitled film.

The shift in the past two years has been substantial. Modern AI translation for in-person meetings now operates in a way that would have felt genuinely futuristic not long ago.

Here is what the workflow actually looks like with a tool like [VideoTranslatorAI](https://videotranslator.ai/).

First, Speech is captured in real time through a microphone as the conversation unfolds. That audio is transcribed almost instantly into text.

Second, the text is translated into the target language and played back as natural-sounding synthesised speech.

The experience resembles a consecutive interpreter: one person speaks a complete thought, the translation follows, and the other person responds. It creates a rhythm that both parties can follow, with enough space to hear, process, and reply without the conversation collapsing into a rushed mess.

![](https://miro.medium.com/v2/resize:fit:1400/1*RxR4rdbe_fQY9FctBibh4g.png)

Critically, what [VideoTranslatorAI](https://videotranslator.ai/) now offers goes beyond translation alone.

The service combines real-time meeting transcription, live interpretation across multiple languages, and automatic meeting summaries into a single workflow.

This means that after the conversation ends, you are not left reconstructing what was agreed or trying to remember which commitment was made in which language.

You have a record. A clear, structured **summary** of what happened, who said what, and what was decided.

For anyone who has ever left a multilingual meeting uncertain about what was actually understood and agreed, this is not a small thing.

![](https://miro.medium.com/v2/resize:fit:1400/1*2ZUQeLyjQJ-4e8l35LZCew.png)

## **The Moments Where This Actually Matters**

![Photo by Georg Arthur Pflueger on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Op1X7TF5ONvZt9yJ "Photo by Georg Arthur Pflueger on Unsplash")

Consider what changes in a few common scenarios.

A general practitioner in a busy urban clinic sees a patient who recently arrived from Vietnam and speaks very limited English.

In the past, that consultation relied on a family member translating, which creates its own problems around accuracy, privacy, and emotional dynamics.

Now, the doctor opens VideoTranslatorAI on a tablet, the conversation proceeds in both languages simultaneously, and at the end, both the patient and the clinic have a summarised record of what was discussed and what was agreed for follow-up care.

A small business owner in Melbourne is hosting a group of potential investors from Japan. The conversation is exploratory, informal, and fast-moving.

Booking a professional interpreter for an initial relationship-building meeting felt like overkill, and the cost was hard to justify before any deal was on the table.

With an AI-powered tool handling the interpretation in real time and summarising the key points afterwards, the meeting happens, the relationship begins, and no one leaves the room with a different understanding of what was discussed.

A community centre in Western Sydney runs weekly drop-in sessions for newly arrived migrants navigating bureaucratic processes. The volunteer running the sessions speaks English and some Mandarin.

The mix of languages walking through the door on any given afternoon is far broader than that. Having a tool that can handle live interpretation across dozens of languages, without pre-booking and without cost-per-session fees, changes what is actually possible.

These are not edge cases. They are the texture of daily multilingual life in a globalised world.

## **The Honest Limits**

It would be dishonest to suggest AI translation tools have solved every problem in this space. They have not.

Highly technical or legally consequential communication still benefits enormously from a trained human interpreter.

Cultural nuance, particularly in contexts where the stakes of a mistranslation are serious, remains an area where AI tools require careful supervision. Accents, dialects, and domain-specific vocabulary can still introduce friction, though the gap between AI and human performance continues to narrow at a pace that is genuinely surprising.

The appropriate framing is not AI versus human interpreters. It is about matching the right tool to the right context.

Human interpreters for high-stakes, complex, or deeply nuanced exchanges where accuracy is critical and budget allows. AI-powered tools for the everyday, spontaneous, or budget-constrained situations that currently go underserved.

The goal is that fewer people leave important conversations having understood less than they needed to.

![](https://miro.medium.com/v2/resize:fit:1400/1*9Ojgyz_PurWOVw6UDFwCYw.jpeg)

## **A Different Kind of Inclusion**

There is a tendency to discuss translation technology primarily as a productivity tool. Faster meetings, smoother deals, better business outcomes. All of that is real.

But the more important story is about **inclusion**.

Language is not just a communication method. It is tied up in dignity, in confidence, in the ability to participate fully in the institutions and decisions that affect your life.

When a person cannot express themselves clearly in an important meeting, it is not just an inconvenience. It is a form of exclusion that compounds over time, across healthcare appointments, school interactions, legal processes, and professional opportunities.

AI-powered in-person translation, done well, does not just make meetings more efficient. It returns the ability to participate to people who have been quietly sitting on the margins of conversations that matter to them.

That is a different kind of value. And it is worth taking seriously.

## **What Comes Next**

The trajectory here is clear. AI translation tools will continue to improve in accuracy, naturalness, and range.

The latency between speech and translation will continue to shrink. The range of supported languages and dialects will continue to expand. The integration with meeting workflows, from transcription to summary to follow-up action items, will become more seamless.

We are still early. But we are past the point where this technology is a novelty or a workaround. For a growing number of professionals, community workers, educators, and healthcare providers, AI-powered in-person translation has moved from a nice-to-have into an operational essential.

The room that almost lost the deal, or the diagnosis, or the relationship, does not have to be that room anymore.

Language should not be the reason someone leaves a conversation having understood less than they needed to. We now have tools that can do something meaningful about that. The question is simply whether we use them.

The Most Dangerous Word in Any Multilingual Meeting

VideoTranslatorAI - The Most Dangerous Word in Any Multilingual Meeting

A beautifully formatted document has a strange power over us. It signals effort. It signals competence. It signals: _someone checked this._

When AI generates that document in four seconds, none of that is true. But our brains don’t get the memo.

This is not a philosophical worry about the future. Anthropic just published data on it.

![Photo by Aerps.com on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*en6X_v2V_OaFVF4S "Photo by Aerps.com on Unsplash")

## **The Finding You Should Not Scroll Past**

![](https://miro.medium.com/v2/resize:fit:1400/0*wV4S0tHlGMVm4KHH.jpg)

On 23rd February 2026, Anthropic released its [AI Fluency Index](https://www.anthropic.com/research/AI-fluency-index): an analysis of nearly 10,000 anonymised Claude conversations from January 2026. The research tracked 11 observable user behaviours, things like questioning the model’s reasoning, checking facts, and identifying missing context.

The headline finding was encouraging. About 85.7% of conversations showed gradual refinement. Users were iterating, pushing back, building on outputs. That looks like healthy collaboration.

Then came the finding that should make everyone stop.

In conversations where Claude produced an artifact — code, a document, an interactive tool — users became markedly **less critical.**

They were 5.2 percentage points less likely to flag missing context. They were 3.7 percentage points less likely to check facts. They were 3.1 percentage points less likely to ask the model to explain its reasoning.

The output looked polished. So people stopped asking questions.

## **The Politeness of A Finished Thing**

![Photo by Priscilla Du Preez 🇨🇦 on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*9-QZPWIIekdsjkAf "Photo by Priscilla Du Preez 🇨🇦 on Unsplash")

There is a deep human instinct at work here. When something looks complete, interrogating it feels almost rude.

We do this with people, too. A colleague who hands you a thick, well-bound report gets fewer probing questions than one who scrawls notes on a whiteboard. The finish signals authority. The authority suppresses scrutiny.

_AI has learned to finish things very, very well._

It formats. It headings. It bullet-points. It uses confident, declarative sentences. It produces documents that look like the output of a careful expert who has done the research, checked the sources, and organised everything for your convenience.

It hasn’t. Not necessarily.

But it looks like it has, and for most human brains, **looking is enough.**

## **The Smarter the AI, the Bigger the Blind Spot**

![Photo by Solen Feyissa on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*rYyl74_dcMOMrxpF "Photo by Solen Feyissa on Unsplash")

Here is where it gets interesting, and a little uncomfortable.

As AI models improve, their outputs get more polished. The prose gets cleaner. The code compiles first try. The documents read more fluently. The hallucinations, when they occur, look increasingly indistinguishable from well-sourced facts.

In other words:

> _The better AI gets at producing finished-looking things, the more our critical instincts disengage._

This is the core of what I’d call the **Competence Trap**. We trust outputs that look authoritative. The more competent AI becomes at generating the appearance of authority, the less we question it.

The less we question it, the more errors, gaps, and confabulations pass straight through our judgement without a scratch.

The [research](https://www.anthropic.com/research/AI-fluency-index) backs this up from the opposite direction too. Users who iterated on their prompts, who pushed and refined and challenged the model, questioned Claude’s reasoning 5.6 times more often and spotted missing context four times more frequently than those who just accepted the first result.

The behaviour that protects you is active friction. The friction disappears when the output looks done.

## **This Is Not Laziness. It Is Cognition.**

![Photo by Jason Strull on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*ednfz_KNE5CU9RzR "Photo by Jason Strull on Unsplash")

It is worth being precise about what is actually happening here, because blaming individuals for being “lazy” or “uncritical” misses the point.

What the Anthropic data captures is a cognitive pattern, not a character flaw.

Automation bias is well-documented: when a system presents confident, coherent output, humans defer to it. This predates AI. It happens with GPS navigation, autopilot warnings, spell-check. We trust systems that project confidence.

What is new is the scale of the confidence AI projects, and the range of domains it projects it across.

A GPS can confidently lead you down a closed road. An AI can confidently give you incorrect medical dosages, wrong legal precedents, fabricated statistics, and code with critical security vulnerabilities, all in the same polished document, all with the same unruffled tone.

The capability has scaled. The critique hasn’t kept up.

## **The Hypothesis, Stated Plainly**

So here is the uncomfortable conclusion:

> **The smarter AI gets, the less we think.**

Not because we are stupid. Because we are human. Because polished things invite trust, and trust invites passivity, and passivity in the presence of a confidently incorrect AI is how errors get embedded into decisions, strategies, code, and documents that ripple through organisations and lives.

The AI Fluency Index was designed to measure whether people are developing the skills to use AI well.

What it found, at least in this slice of data, is evidence of a gap between the sophistication of the tool and the critical engagement of the user, a gap that widens when the tool performs best.

That should land as a wake-up call, not a reason to slow down AI adoption, but a reason to be deliberate about how we engage with it.

## **What To Do With This**

![Photo by Rob Coates on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*9govNOAIlD2E822e "Photo by Rob Coates on Unsplash")

The Anthropic data offers a clue in its own findings. Users who iterated and refined, who treated the model as a collaborator to interrogate rather than an oracle to accept, were far more likely to catch what was wrong. The behaviour that produces better results is also the behaviour that keeps your thinking sharp.

A few things worth practising:

**Ask before you accept.** When an AI produces a polished artifact, that is exactly the moment to slow down, not speed up. The polish is not evidence of accuracy.

**Name the gaps.** Before you use any AI output, spend sixty seconds asking what it might have missed, mischaracterised, or fabricated. Make it a habit, not a reaction.

**Demand the reasoning.** Ask the model to explain how it got there. Not because the explanation proves accuracy, but because your engagement with the explanation keeps your own thinking active.

The goal is not to distrust AI. The goal is to remain the one doing the thinking.

Because here is the thing: **AI doesn’t get dumber when you stop checking. You do.**

The Smarter Your AI, The Dumber You Get - Tat Banerjee

The Smarter Your AI, The Dumber You Get

A few days ago, I wrote about how [powerful AI](https://videotranslator.ai/blog/ai-will-take-half-of-white-collar-jobs-in-5-years) has become. Not powerful in the Silicon Valley press-release sense, but genuinely, structurally powerful in ways that most people are still underestimating.

The capability is real. The acceleration is real. The implications for how work gets done, how industries organise themselves, how value gets created, all of it is already in motion.

I meant that piece as a signal to pay attention. And this piece is about what you should be paying attention to.

Because a scenario is quietly forming alongside all that capability, and it does not come from AI being bad at its job. It comes from AI being extraordinarily good at it, inside an economic system nobody has redesigned to account for that goodness.

[Citrini Researc](https://www.citriniresearch.com/p/2028gic)h amplified it. Their viral 2026 piece gave it a name, a mechanism, and a number and put it in front of the people who needed to hear it most.

> They called it **Ghost GDP.**
> 
> ## **What Ghost GDP Actually Means**
> 
> ![](https://miro.medium.com/v2/resize:fit:1400/1*EhnK3K0rfIgwPIzqd9YW1Q.png)
> 
> Let me explain Ghost GDP with an analogy a high schooler could follow.
> 
> Imagine your school replaces the canteen staff with a single giant vending machine.
> 
> The machine is extraordinary: it serves 500 students a day, never calls in sick, never asks for a pay rise, and runs on a fraction of what the canteen workers used to cost. From the school’s perspective, productivity has exploded. Output per dollar spent has never been higher.
> 
> But here is what the school’s financial report will not show you.
> 
> The five canteen workers who lost their jobs no longer buy lunch at the local cafe. They stop visiting the weekend markets. They cancel their streaming subscriptions. The local economy, quietly and without fanfare, contracts a little. **The machine generates output. The machine spends nothing.**
> 
> Now scale that up to an entire country. That is Ghost GDP.
> 
> Economic output that appears in the national accounts gets celebrated in earnings calls and productivity reports, but never actually circulates through the hands of ordinary people.
> 
> It is growth that ghosts the real economy. Present on paper. Absent from the checkout.

## **The 2026 Economy is Already Showing the Symptoms**

![Photo by Possessed Photography on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*iITQ_1iPymdCbKPS "Photo by Possessed Photography on Unsplash")

[Citrini](https://www.citriniresearch.com/p/2028gic) wrote their piece as a memo from 2028, looking back at how the crisis unfolded. But the conditions they described are not waiting for 2028 to arrive.

Their scenario modelled what happens when AI agents replace roughly 70% of white-collar “inefficiencies” across the US economy.

Lawyers, financial analysts, accountants, marketing professionals, junior developers: not just assisted by AI, but replaced by it.

White-collar employment, the sector that underpinned a generation of mortgage approvals and consumer confidence, starts to hollow out.

From there, the cascade runs predictably. Reduced income leads to reduced consumer spending.

Consumer spending reductions translate into private credit stress. Private credit stress radiates into mortgage markets. The $13 trillion US mortgage market begins to fracture because it was written against income levels that AI displacement has structurally impaired.

Meanwhile, stock markets, which had been rising on the back of AI-driven corporate profit gains, suddenly have to reprice for a consumption collapse.

Citrini modelled the S&P 500 falling 38% from its October 2026 highs. Unemployment reaching 10.2%.

Not because AI failed. Because it succeeded, in a system nobody redesigned to handle that success.

## **Agentic AI Makes This Urgent, Not Theoretical**

![Photo by Israel Andrade on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*YVZw1mTfOF38EJJV "Photo by Israel Andrade on Unsplash")

Now, let’s sit and talk about Agentic AI that’s zipping along faster than a jet.

Agentic AI does not just assist. It executes. It researches, drafts, formats, files, routes, and decides, across multi-step workflows, without a human touching each step.

Legal departments, finance functions, content teams, and software organisations are deploying it right now, not in pilots, but in production.

The work that used to require three or four junior professionals now requires an AI agent and one person to supervise it. That supervision role is itself under pressure as models get more reliable.

This is the structural setup that Citrini’s Ghost GDP scenario requires. Not a distant AI takeover.

Just a steady, quarterly replacement of billable human hours with agent hours, compounded across industries, compounded over time, until the river of economic circulation runs slower and slower.

What makes agentic AI different from previous automation waves is the breadth. Factory automation displaced blue-collar workers. Agentic AI targets exactly the white-collar professionals who were supposed to be safe: educated, credentialed, employed in the information economy.

The mortgage-paying, locally-spending, SaaS-subscribing workforce that holds consumer economies together.

## **Keeping Humans in The Loop**

![](https://miro.medium.com/v2/resize:fit:1400/1*Y20MqTVUQhp3duaZxH5u8Q.png)

Here is what I want to say directly:

> **The most important conversation in AI right now is not about capability. It is about deployment structure.**

We have been so focused on what AI can do that we have barely started asking how it should be integrated into systems that still require human economic participation to function.

Keeping humans in the loop is not a safety slogan. It is not a regulatory box to tick. In the context of Ghost GDP, it is a structural intervention in the economy itself.

When humans remain part of the production process, they receive income. When they receive income, they spend it. Spending powers the small businesses, the services sector, the housing market, the tax base, the entire downstream of a functioning society.

Remove the human from the loop, and you do not just lose a job. You lose a node in a circulation network that has been running since the first marketplace.

[Fortune’s](https://fortune.com/2026/02/23/will-ai-take-my-job-cause-recession-crash-james-val-geelen-citrini/) coverage of Citrini’s work raised the same question that their own research posed: what actually prevents the Ghost GDP scenario from becoming real?

The answer is not AI regulation. It is not a moratorium on automation. It is deliberate design choices, made by organisations and policymakers, about what role humans should continue to play.

AI that augments human workers distributes productivity gains. AI that eliminates human workers concentrates them.

The difference between those two paths is a choice, and in many organisations, that choice is being made this quarter, in workforce planning meetings, without anyone framing it in those terms.

## **What This Requires of Us**

![Photo by Nahrizul Kadri on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*64dwr_K8pnZApFii "Photo by Nahrizul Kadri on Unsplash")

[Citrini Research](https://www.citriniresearch.com/p/2028gic) was careful to say their piece was a scenario, not a prediction. They were not writing AI doom fiction. They were mapping a credible risk that deserved serious modelling.

That framing matters. The Ghost GDP outcome is not inevitable. But it requires active work to avoid. And that work starts with honesty about where we currently are.

We have built AI systems of extraordinary power. As I argued in my previous piece, that power is real and it is accelerating.

But power without considered deployment is not progress. It is just a faster way to arrive at unintended consequences.

The concrete steps matter here.

**Organisations** can design AI deployment with deliberate human roles: not token oversight, but genuine participation at the points where judgement, context, and accountability are highest value.

**Policymakers** can ask whether economic indicators are measuring circulation, not just output.

**Researchers** and **executives** can pressure-test automation decisions against the question: does this preserve the economic loop, or does it extract value from it?

Citrini named the ghost. That is the first step.

Now the people building and deploying AI need to decide whether they are going to keep haunting the economy, or design something that actually lives in it.

* * *

_“THE 2028 GLOBAL INTELLIGENCE CRISIS: A Thought Exercise in Financial History, from the Future” was published on 22 February 2026 by Citrini Research, co-authored by Citrini and Alap Shah. Available at_ [_citriniresearch.com_](http://citriniresearch.com)_._

_Highly recommended reading for anyone thinking seriously about AI and economic risk._

Welcome to the Ghost GDP Economy - Tat Banerjee

Welcome to the Ghost GDP Economy

We have been telling the negative story about AI and education a lot.

Pick up any newspaper, open any parent forum, and you will find the same anxiety playing out in a loop:

> AI gives students instant answers, students stop thinking, students become intellectually dependent and academically dishonest.

The villain is clear. The verdict is in. AI is making the next generation dumber.

I have heard that story so many times that I almost started believing it myself.

Then I came across a piece by the team at [Every](https://every.to/podcast/inside-an-ai-high-school-through-the-eyes-of-a-17-year-old-founder), where Dan Shipper sat down with Alex Mathew, a 17-year-old high school senior in Austin, Texas. What followed was one of the most optimistic conversations about the future of education I have encountered in a long time.

I felt a genuine urge to amplify it, because it deserves a wider audience than it may otherwise reach.

## **A School That Turned The Classroom Inside Out**

![](https://miro.medium.com/v2/resize:fit:1400/0*5TVvFxio11uK7q0m)

Mathew attends [Alpha High School](https://alphahigh.school/), part of a rapidly expanding network of kindergarten-through-grade-12 private schools that have made a radical decision: **no traditional teachers.**

Academic content is delivered entirely through an AI-powered learning platform called **_Timeback_**. The adults in the room, known as “guides,” do not teach curriculum. Their job is to support students emotionally and keep them motivated.

The school day begins at 8:30 a.m. with what Mathew describes as “Tony Robbins for kids.” Before any academic work starts, students spend 15 minutes shifting from “home mode” to “school mode.”

They might debate an LLM, solve a puzzle, or discuss something interesting they found on X. Students are grouped into “houses,” Hogwarts-style, based on personality and the progress they have made on their individual projects.

Then comes the academic block: two to three hours of focused learning, structured into 27-minute intervals with five-minute breaks. Timeback delivers a personalised stream of videos, articles, and quizzes, some built by Alpha, others sourced from third-party providers. AI runs quietly in the background, customising what each student sees and tracking where their understanding has gaps.

And crucially, there is **no AI chatbot tutoring anyone**. Alpha tested that approach but it did not work.

When students had open access to a chatbot, they used it to cut corners. When the chatbot was restricted, it became useless. So the school moved on.

The AI serves the _learning architecture_. It does not replace the student’s thinking.

## **Motivation is The Real Curriculum**

![Alex Mathew, a 17-year-old student from Alpha High School. Source: Every](https://miro.medium.com/v2/resize:fit:1400/1*fJembDG7ez-dbeO72bQN6Q.png "Alex Mathew, a 17-year-old student from Alpha High School. Source: Every")

Here is the insight from Mathew that has stuck with me: “It is 90 percent motivation, 10 percent education technology.”

That ratio matters. The school does not rely on AI to do the heavy lifting. It relies on genuinely motivated students. The AI is the infrastructure. The drive to learn still has to come from somewhere human.

Alpha builds that motivation in several ways. For subjects a student loves, the content itself is the reward.

Mathew breezes through AP Psychology because it connects directly to his startup, a company called [Berry](https://berryplush.com/) that he is building around an AI-stuffed animal designed to help teenagers manage their mental health.

@[youtube](https://www.youtube.com/watch?v=9OS1bvr1p94)

For subjects students dislike, the school gets inventive, offering students money toward their projects or invitations to social outings, called “FOMOs,” when they hit their targets.

The school also gives students something most educational institutions never offer: **genuine agency.** Mathew negotiated with his guides to complete his first semester early so he could fly to San Francisco and work on Berry full-time, then return to finish his second semester without losing credits.

That is not a loophole. That is the design.

## **The Barrier that Motivation Alone Cannot Solve**

![Photo by Taylor Flowe on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*I5wFLWdF3a9KGcxg "Photo by Taylor Flowe on Unsplash")

Reading about Alpha High School, I kept thinking about a different kind of barrier: **not the absence of motivation, but the absence of access.**

I recall coming across [Nippon.com](http://Nippon.com)’s article last year about the growing need for specialised Japanese language instruction in Japan. As of the fiscal year 2023, nearly 70,000 students in elementary, junior high, and high schools required this support, a number that has more than doubled over the past decade.

Interestingly, in 70 percent of the schools where these students are enrolled, there are only four or fewer students needing such instruction. This small ratio makes it challenging for local governments to justify dedicating specialised staff to address the issue effectively.

In fiscal 2023, 970 school-age foreign children were confirmed not to be attending school at all. That represents a 24.6 percent increase over the previous year.

Taking into account children whose attendance status was unclear, the number of young people potentially excluded from education entirely may reach 8,601.

These are not students lacking motivation. They are students locked out of a learning environment because it operates in a language they cannot yet follow.

The AI-enhanced classroom that Alex describes at Alpha High could not help them if the lesson itself is inaccessible.

That is the gap [VideoTranslatorAI](https://videotranslator.ai/) was built to close

## **Real-time Translation, Built for Education**

Our multilingual **video call** platform allows students and teachers to speak in their own language during live sessions. AI handles real-time translation for every participant simultaneously.

Every person in the session receives subtitles in their chosen language as the lesson progresses. A natural voice-over translation also runs in parallel, which participants can activate or mute depending on how they prefer to learn.

In practice, this means a teacher conducting a session in English can be understood in real time by a student following along in Portuguese, another in Vietnamese, and a third who has switched on German voice-over.

No lag. No parallel tracks. No student sitting in silence because the content moved faster than their ability to translate.

The platform extends to **multilingual broadcasting** for larger events: universities, global training programmes, and educational conferences. A single session streams to hundreds of participants, each receiving live subtitles and voice-over in the language of their choice. The institution runs the session once. The translation happens automatically.

![Multilingual broadcast with VideoTranslatorAI](https://miro.medium.com/v2/resize:fit:1400/1*OXlR2HoNvmVz1tBlStM3Eg.png "Multilingual broadcast with VideoTranslatorAI")

The connection to what Alpha High School has built is not just thematic. Both approaches rest on the same conviction: **friction is the enemy of learning.**

At Alpha, the friction is a rigid structure that does not adapt to individual students. In the classrooms VideoTranslatorAI serves, the friction is language.

Remove the friction, and students can finally get to the actual work of thinking, growing, and making sense of the world.

## **Why This Story Deserves A Wider Audience**

@[youtube](https://www.youtube.com/watch?v=n-xBQE8A-3E)

I am grateful to Dan Shipper and the Every team for the episode that sparked this piece. What they offered was something rare: a conversation about AI and education conducted with the people actually inside it, rather than the people theorising about it from a distance.

Alex Mathew is building a startup at 17. He is thinking carefully about the tradeoffs in his generation’s relationship with technology. He is navigating his own education with more intentionality than most adults bring to their careers. And he is doing it at a school that trusted him enough to let him design his own path through it.

That is the story worth repeating. Not because AI is a panacea. Not because every school should look like Alpha. But because when technology is designed around what students actually need, rather than what is administratively convenient, genuinely remarkable things become possible.

At VideoTranslatorAI, we are trying to do the same thing for language. Because the student who cannot follow the lesson should never be written off as uninterested.

They may simply have never had the chance to understand what was being said.

* * *

_This piece was inspired by the AI & I podcast episode “_[_Inside an AI High School, Through the Eyes of a 17-Year-Old Founder,_](https://every.to/podcast/inside-an-ai-high-school-through-the-eyes-of-a-17-year-old-founder)_” produced by Dan Shipper and the Every team. It features a conversation with Alex Mathew, a student at Alpha High School in Austin, Texas. The full episode is available at_ [_every.to_](http://every.to)

_Highly recommended listening!_

He’s 17, Runs a Startup, and Designs His Own Curriculum

Yesterday, February 24th 2026, Anthropic posted a tweet accusing three Chinese AI companies of theft.

The internet read it, remembered something, and _started laughing_.

The replies are hilarious. And this is one of my favourites:

![Source: @AskOkara on X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/9f0f1ec03a0fd600a533fa662926daa553bd41b52b9c247fee0501b170b78a29.avif "Source: @AskOkara on X")

Five months earlier, Anthropic had paid $1.5 billion to settle a class-action lawsuit brought by book authors who argued, convincingly enough for a nine-figure payout, that Anthropic had used pirated copies of their works to train Claude. No admission of liability. Just $3,000 per book and a quiet resolution.

![AI startup Anthropic agrees to pay $1.5bn to settle book piracy lawsuit](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/ce74d4fda1cec6f680529214ff6089894a2febe8f254d38d6673587b4a5a9a19.avif "AI startup Anthropic agrees to pay $1.5bn to settle book piracy lawsuit")

Read the original article: [The Guardian](https://www.theguardian.com/technology/2025/sep/05/anthropic-settlement-ai-book-lawsuit)

The tweet went out anyway.

![Anthropic original post on X ](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/bd9bdd5ac00cf6859c8335055b7f5812b08a6a173fc319f6a3ebc8b0f711dffc.avif "Source: @AnthropicAI via X")

Well, according to Anthropic, DeepSeek, Moonshot AI, and MiniMax created over 24,000 fraudulent accounts and generated more than 16 million exchanges with Claude, systematically extracting its capabilities to train their own models.

[Fortune](https://fortune.com/2026/02/24/anthropic-china-deepseek-theft-claude-distillation-copyright-national-security/) borrowed a line from The Office for its headline: “How the turn tables.”

[OpenAI](https://www.reuters.com/world/china/openai-accuses-deepseek-distilling-us-models-gain-advantage-bloomberg-news-2026-02-12/) had, in fact, moved first. Around two weeks earlier, Sam Altman’s company warned US lawmakers that DeepSeek was distilling outputs from American models to train its own.

[Anthropic’s](https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks) public accusation followed. The frontier AI labs had both decided, independently or close enough to it, that this was the moment to draw a firm line. What makes that line so interesting is where it sits, and who drew it.

## **What Distillation Actually Means**

![Source: WIRED](https://miro.medium.com/v2/resize:fit:1400/0*fFID-P7vAxn67aqf.jpeg "Source: WIRED")

Distillation, in AI terms, is not inherently sinister. It describes the process of using a large, capable model to generate outputs that then train a smaller or different model.

Done legitimately, it compresses intelligence into cheaper, faster systems. Most of the AI industry does some version of this.

The accusation here is not about distillation itself. It is about the scale and the deception: 24,000 of fake accounts, millions of interactions, all designed to extract the intellectual core of a competitor’s product without paying for it.

Anthropic’s tweet made a specific point about national security. “_Foreign labs that illicitly distill American models can remove safeguards_,” they wrote, “_feeding model capabilities into their own military, intelligence, and surveillance_” infrastructure.

This framing matters. It transforms a commercial grievance into a geopolitical one, shifting the story from corporate rivalry to national interest. That is a very effective way to ensure regulators and policymakers pay attention.

## **The Glass House Problem**

![Photo by YUNUS EMRE ILICA on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*g4sDBvsgsU6qOfMM "Photo by YUNUS EMRE ILICA on Unsplash")

The settlement context is worth sitting with for a moment.

Anthropic did not invent its training methodology in a vacuum. [Claude](https://hai.stanford.edu/news/be-careful-what-you-tell-your-ai-chatbot) was trained on vast quantities of text, and a significant portion of that text was written by people who were never asked, never paid, and never informed.

When those [authors](https://copyrightalliance.org/participating-bartz-v-anthropic-settlement/) organised and sued, Anthropic settled for what was, at the time, one of the largest AI-related legal payouts on record.

This does not make the Chinese companies’ alleged behaviour acceptable. Using fake accounts to systematically drain a competitor’s model is deceptive in a way that training on publicly available text, however ethically murky, is not. The methods are different. The scale of deception is different.

But the underlying dynamic, taking something valuable from someone without permission and using it to build your own capability, is the same pattern. Anthropic benefited from that pattern once. Now they are on the other side of it.

The industry has a short memory for its own sins and a long memory for everyone else’s.

## **The Playbook is Always the Same**

![Photo by Adlan on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Tino46fFTWP2WYP7 "Photo by Adlan on Unsplash")

This isn’t new behaviour. It’s not even surprising. It’s the oldest strategy in competitive industries:

_“Accuse your rivals of the exact things you do yourself, but frame your actions as innovation and theirs as theft.”_

Before we had AI labs accusing each other of distillation, we had the “War of the Currents” between Thomas Edison and Nikola Tesla (technically Westinghouse, but the mythology prefers Edison versus Tesla).

Edison ran smear campaigns claiming alternating current was deadly, publicly electrocuting animals to prove AC’s danger. He even suggested AC be used for the electric chair to associate it with death.

Yet Edison’s methods weren’t pristine either. His “invention factory” in Menlo Park operated on the principle of taking employees’ ideas, developing them into patents under his name, and claiming credit for innovations that emerged from collaborative work.

The myth that Edison stole Tesla’s ideas persists because the underlying pattern is familiar: established players using every tool to discredit challengers threatening their dominance.

The tech industry repeated this pattern endlessly. When Google launched Android in 2008, Apple’s Steve Jobs called it “grand theft” and vowed “thermonuclear war” against what he saw as blatant copying of iOS.

Apple sued Samsung for copying iPhone designs. Samsung countersued for patent violations. The lawsuits dragged on for years, costing both companies billions.

The outcome? **Both companies kept selling phones. Both kept making money.**

The “theft” accusations were theatre in a larger competitive game where everyone copies everyone, but only your rivals’ copying is theft.

## **Power Protects What Power Builds**

![Photos from AI Impact Summit 2026](https://miro.medium.com/v2/resize:fit:1400/0*DIkknb8Z1dUVX5vt "Photos from AI Impact Summit 2026")

There is a deeper logic at work across all of these cases. The entity that establishes a dominant position eventually uses every available lever to protect it.

The challenger, who often built their capability by standing on the incumbent’s shoulders, finds those tools turned against them.

The [Deep View,](https://www.thedeepview.com/articles/anthropic-flags-chinese-models-for-stealing) a newsletter tracking the AI industry, described this moment as American frontier labs trying to “_fend off Chinese model makers from exfiltrating the frontier labs’ crown jewels._”

That framing is revealing. Crown jewels are not earned through moral purity. They are simply what the most powerful player currently possesses.

Anthropic, OpenAI and Google are not wrong to object to what they allege happened. Industrial-scale deception, fake accounts, systematic capability extraction: these are genuine violations of terms of service and arguably of law. The complaints are legitimate.

But the moral authority of the complaint is complicated by the path each of these companies took to get here. [OpenAI](https://www.euronews.com/next/2024/01/09/openai-says-its-impossible-to-train-ai-without-copyrighted-materials) trained on internet data that included copyrighted content at scale. [Google](https://en.wikipedia.org/wiki/Google_litigation#:~:text=Google%20has%20been%20involved%20in,of%20Corporate%20Development%20David%20Drummond.) has faced decades of intellectual property litigation across its products. Anthropic just paid $1.5 billion to the people it quietly borrowed from.

The rules of the game tend to get formalised right around the moment that the rule-makers stand to benefit from having rules.

## **What This Moment Actually Tells Us**

![Photo by Robynne O on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*KbPWuGdsteoX16J- "Photo by Robynne O on Unsplash")

The distillation accusations are significant regardless of the irony. They signal that frontier AI has reached a stage where capability gaps between American and Chinese labs have narrowed enough to make this kind of extraction worthwhile.

If DeepSeek, Moonshot AI and MiniMax were so far behind that Claude’s outputs offered little competitive value, the alleged campaigns would not have been worth running.

The accusations also reveal how the AI race is actually being fought. It is not only about compute, data and research talent. It is about access to the output of your competitor’s model at scale.

Distillation has become a form of competitive intelligence gathering. That changes what security means for an AI company.

Anthropic’s national security framing may well be sincere. The concern that distilled models, stripped of safety guardrails, could power military or surveillance applications is legitimate and worth taking seriously.

But it is also strategically useful to frame a commercial dispute as a matter of national defence. Governments move faster and more forcefully when the word “intelligence” appears in the brief.

## **The Uncomfortable Conclusion**

![Photo by Vitaly Gariev on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*OK8ItpTuBwRJO25C "Photo by Vitaly Gariev on Unsplash")

Every major technology transition produces this moment. The people who broke the old rules to build new power eventually write new rules to protect what they built.

Tesla’s AC power rewired the world after Edison tried to kill it. Android phones reached three billion users after Apple tried to litigate them out of existence.

The pattern does not determine the outcome. It just tells you what stage of the game you are watching.

Right now, the American frontier labs are watching their lead narrow and reaching for every available tool: legal, political, national security framing, public accusation.

Some of those tools are justified. Some of those tools are the kind they once found pointed at themselves.

The Chinese labs, whether guilty of the specific accusations or not, are doing what challengers in every technology race have done. They are climbing using whatever handholds the current leaders left exposed.

History suggests the leaders rarely hold the lead forever. It also suggests that the moral accounting, when it finally arrives, tends to be distributed fairly evenly across both sides.

Anthropic Just Accused China of What It Got Sued For

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