> The cheap AI era is coming to an end.

I believe most people are already aware, but let me try to explain it in the simplest way possible.

Picture your favourite **_all-you-can-eat buffet_**. The first time you go, it is incredible. Endless plates. One low price. You go back the next week. You bring your friends. Your friends bring their friends. The place is packed every night, and the food just keeps coming.

Then one day you walk in and the manager is standing at the door with a slightly uncomfortable expression. The crab legs are gone. The prawns have a serving limit. There is a new premium tier for the good stuff. The sign on the window still says _all-you-can-eat_, but something has clearly changed.

This is exactly what is happening with AI right now.

For the past two years, using AI felt like a magical, unlimited buffet. You could chat, generate, analyse, summarise, code, and create as much as you wanted for a flat monthly fee. The food kept coming. And behind the scenes, the restaurant was losing money on every plate.

That era is ending. And the reason it is ending tells you a lot about where AI is heading next.

## **How the AI Buffet Got Started**

![AI buffet illustration (Created with Canva AI)](https://miro.medium.com/v2/resize:fit:1400/1*6XH5LoyBF8EmAcMokDf82A.png "AI buffet illustration (Created with Canva AI)")

To understand why the buffet is changing, it helps to understand how it was funded in the first place.

When OpenAI launched ChatGPT Plus in 2023, the pricing was deliberately attractive. The goal was not to make money immediately. It was to get as many people as possible using the product, so they would form habits, build workflows, and become the kind of customer who would pay more later.

Think of it like Uber offering heavily discounted rides in a new city, or Costco handing out free samples at every corner of the store. The price is a **_customer acquisition strategy_**, not a reflection of actual costs.

It worked extraordinarily well. ChatGPT reached 100 million daily active users faster than any consumer app in history. Anthropic’s revenue grew 44 times in just 15 months. Millions of people signed up for flat-rate subscriptions and started using AI for everything.

The economics were sustainable because of how people naturally use AI in conversation. You type a message. You read the response. You think. Maybe you type another message five minutes later. The AI is only actively working during those brief exchanges. The rest of the time, the server is idle. Flat-rate subscriptions work when usage is spread out and human-paced, because you can average the costs across millions of people who use it at different times.

This is the buffet logic: nobody eats non-stop. Most customers take a plate, sit down, eat slowly, and leave. The restaurant can price for the average customer because the outliers do not break the model.

Then came the **_agents_**.

## **AI Agents: The Customers Who Never Left**

![The greedy AI agent (created with Canva AI)](https://miro.medium.com/v2/resize:fit:1400/1*hhRydzrT-fzNpwDy2tsNaQ.png "The greedy AI agent (created with Canva AI)")

An AI agent is not like a chatbot. You do not have a conversation with it and then go about your day. You give it a goal, and it works toward that goal continuously, taking actions, checking results, correcting mistakes, running searches, writing code, sending requests, and retrying when something fails. Over and over, without stopping to wait for you.

In buffet terms, the other customers left after dinner. The agents stayed. And they **kept eating all night.**

This changes the economics completely. One agent working on a complex software project for a day might make thousands of API calls. Each call consumes compute, which costs money.

The flat monthly subscription that made sense for a human having a few conversations per day makes very little sense for an agent running thousands of operations per hour.

The numbers bear this out in uncomfortable ways. One developer’s coding project with Claude ran up costs exceeding $150,000 in a single month through API usage. A power user documented by The New York Times burned through 1.8 million tokens in a single month, racking up a bill of $3,600. These are not bugs or misconfigurations. They are a preview of what normal agentic usage looks like at the infrastructure level.

As Eric Broda wrote in his Substack piece “[_The End of Token Subsidies_](https://agenticmesh.substack.com/p/the-end-of-token-subsidies)”:

> _“For two years, companies treated subsidised AI pricing as if it were durable economics. It was not. It was promotional pricing for a new kind of compute habit.”_

That promotional period is over. The kitchen is starting to ask questions about where all the food went.

## **Who Was Paying for All of It?**

![Photo by Logan Voss on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*oFtDG_O488sNM7eu "Photo by Logan Voss on Unsplash")

The honest answer is: mostly **investors**.

OpenAI has raised over $17.9 billion and is not yet consistently profitable despite enormous revenue. Anthropic has raised billions on the premise that it will eventually capture enough of the market to justify the losses.

The data centre buildout required to run these models at scale costs tens of billions of dollars in capital expenditure. Someone is funding the gap between what AI costs to run and what users actually pay.

For now, that gap is funded by venture capital, tech giants with deep pockets, and increasingly by sovereign wealth funds and government-adjacent investors who see AI infrastructure as strategic. Governments have indirectly contributed through tax incentives for data centre construction and energy infrastructure.

This is not a criticism. It is how new technology industries work. The telephone network was not profitable for decades. The internet required massive subsidies before it became commercially self-sustaining.

The subsidies create adoption, adoption creates data and capability, and capability creates a product worth paying a real price for.

But subsidies have to end eventually. Investors want returns. The losses cannot grow indefinitely. And the arrival of agents has dramatically accelerated the timeline on which that reckoning arrives.

## **What the Buffet Looks Like Now**

The changes are already underway, even if they are not always announced in big press releases.

Anthropic quietly tested removing Claude Code from its standard $20 per month Pro plan in early 2026. Claude’s Max plan now costs $200 per month and is positioned explicitly for heavy users.

![Source: George Pu via X](/api/media/elephantstripes-prod/cms/originals/blog-inline/c93d9609d04c6b318c453e6846b2c9bb557384d85e6beeea71e7ea5c5c2b3d00.avif "Source: George Pu via X")

OpenAI has introduced usage limits on its most capable models and launched higher tiers for users who need more. The era of genuinely unlimited access at a fixed low price is narrowing.

The direction of travel is clear. AI is moving toward **usage-based** billing models where heavy consumers pay proportionally more. There will be different tiers for access, with various features costing different amounts. Users will have better tools to monitor and manage their AI spending before receiving their bills.

Is this bad news? Not necessarily.

When companies are forced to operate more efficiently, they tend to innovate. Smaller, smarter AI models are already appearing that can do specific jobs at a fraction of the cost of the big general-purpose ones. The pressure to be profitable is pushing the industry toward more sustainable engineering choices.

But there’s no getting around the other part of the picture either. As Ed Zitron's analysis pointed out:**more compute does not make OpenAI or Anthropic’s services cheaper to offer**. The path to profitability goes through higher prices.

The buffet is switching to “pay by the plate.” For light, occasional users, that might not change much. For heavy users and businesses running autonomous agents around the clock, the bill is about to look very different.

## **What This Means for You**

![Photo by Compagnons on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*oGGYecg31E8NbbbK "Photo by Compagnons on Unsplash")

If you are an individual user, the practical impact depends on how you use AI.

**Casual users** who chat with AI occasionally and use it for occasional writing, research, or creative projects will probably not notice much difference. Standard tiers will continue to exist at reasonable prices. The changes are aimed at the heavy end of usage.

If you are a **developer** or **small business owner** who is starting to build with agents, this is the moment to start paying attention to your AI costs the same way you pay attention to your cloud infrastructure costs. Set budgets. Monitor usage. Build with efficient models where the task does not require the most capable option available. The habit of treating AI spending as a variable cost to be managed rather than a flat subscription to be ignored is one worth developing now, before it becomes urgent.

For **businesses** already running significant AI workloads, the advice is to audit what your agents are actually doing, identify where compute is being consumed unnecessarily, and build approval workflows for tasks that might trigger large unexpected bills.

For **everyone**: AI is not going away. It is growing up. It is moving from a magical technology subsidised by venture capital into something more like a real utility, with real bills, real pricing tiers, and real consequences for wasteful usage. This is what maturity looks like in a technology industry.

## **The Buffet Will Still Be Open**

![Photo by Igor Omilaev on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*wj7Z4apKvyIDqARB "Photo by Igor Omilaev on Unsplash")

The unlimited era was genuinely fun while it lasted. Cheap, powerful, always-on AI changed how millions of people work, create, and solve problems. That shift is permanent regardless of what happens to pricing.

What is changing is the economic model underneath it. The kitchen is becoming more honest about what things actually cost. The all-you-can-eat sign is coming down in some places and being replaced with something more like a good restaurant: excellent food, clear prices, and the expectation that serious eaters will pay a serious bill.

That is not a tragedy. That is sustainability.

The tools will keep improving. The prices will keep coming down per unit of capability. And the companies that figure out how to use AI thoughtfully, measuring what they use and optimising how they use it, will find they are spending less and getting more than the ones who treated the buffet as something that would last forever.

Nothing does. But the kitchen is still open.

* * *

_Check out Eric Broda’s “_[_The End of Token Subsidies_](https://agenticmesh.substack.com/p/the-end-of-token-subsidies)_” and Ed Zitron’s “_[_AI Is Too Expensive_](https://www.wheresyoured.at/ai-is-too-expensive/)_” on Substack. I recommend these reads for anyone involved in AI infrastructure._

Why Your AI Subscription Feels Different Now

The truth about the AI buffet model that was never meant to last

The truth about the AI buffet model that was never meant to last. The cheap AI era is ending. I believe most people are already aware, but let me explain it simply

Read practical notes from Elephant Stripes on AI product delivery, automation, language technology, and useful software systems.

Blog | Elephant Stripes

Practical notes from Elephant Stripes on AI product delivery, automation, language technology, and the operational decisions behind useful software.

Blog

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**

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

Paid $1.5B for theft. Now screaming about theft. Yesterday, February 24th 2026, Anthropic posted a tweet accusing three Chinese AI companies of theft.

Half of all white-collar jobs could disappear in five years.

That’s not a doom-scrolling headline from a tech-sceptic newsletter. That’s Dario Amodei, the CEO of Anthropic, the company building Claude, one of the world’s most capable AI systems.

He said it to [NBC News](https://www.nbcnews.com/video/anthropic-ceo-dario-amodei-warns-of-powerful-ai-risks-256663621646) in January. He published a 20,000-word [essay](https://www.darioamodei.com/essay/the-adolescence-of-technology) around the same time framing this period as humanity’s “technological adolescence,” a rite of passage we have to survive without destroying ourselves.

So, is your job actually fine?

Probably. But your habits might not be.

## **What He Actually Said**

@[youtube](https://www.youtube.com/watch?v=tjW_gms7CME)

The most-shared version of Amodei’s statement strips out the second half of his argument. People grabbed the headline, _white-collar jobs gone in five years_, and ran with it.

Here’s what got left behind.

Amodei also said the critical response is to create new work faster than AI destroys existing work. Not slower. Not at the same rate. Faster.

That’s not a passive message. It’s a directive. And it implies something uncomfortable: the risk isn’t just that AI is coming for your role. The risk is that you’re not moving quickly enough to build something AI can’t absorb.

The disruption is real. The defeatism is optional.

## **The Habit Problem**

![Photo by Srinivasan Venkataraman on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*-1ORId3dpQLB-Vl5 "Photo by Srinivasan Venkataraman on Unsplash")

Think about the last week of work. How much of it was genuinely irreplaceable?

Not valuable. Not well-paid. Irreplaceable.

How much was mechanical? Processing information that already existed somewhere in a system? Reformatting, summarising, scheduling, filing? How much required your specific human judgement, your ability to read a room or navigate a relationship or make a call that no algorithm could verify?

For most white-collar workers, that breakdown is uncomfortable. A significant portion of daily work sits in the mechanical column. And that’s the portion AI is already absorbing.

The habit problem isn’t laziness. It’s that most people are operating with a skill stack built for a world that’s shifting underneath them.

They’re excellent at work that looks productive but is increasingly automatable. And they haven’t had to think hard about that distinction until now.

Amodei’s advice to students and professionals is consistent: lean toward human-centred skills. Get comfortable directing and managing AI rather than competing with it. Cultivate the critical thinking to know when AI output is right and when it’s subtly, confidently wrong.

## **AI Isn’t the Problem. Avoidance Is.**

![](https://miro.medium.com/v2/resize:fit:1252/0*O9kdumKuiAY_N-5s.jpg)

I’ve written before that AI is like a [magic wand](https://medium.com/@tathagat.banerjee/we-dont-have-an-ai-crisis-we-have-an-accountability-crisis-7b7e2eab21b9). It amplifies whatever the wielder intends. The real crisis around AI isn’t technological. It’s about accountability.

AI doesn’t make decisions. People do. The tool follows the direction of whoever wields it.

The same logic applies here. AI doesn’t choose which jobs to absorb. It absorbs the work that humans point it at and the work that humans never chose to protect by developing what makes them irreplaceable.

This isn’t a criticism. It’s a map.

If you understand what AI is actually good at, which is high-volume, pattern-based, language-processing tasks, you can make deliberate choices about where you spend your energy.

You lean into the work that requires judgement, trust, relationships, and context. You use AI to handle the rest. That’s what you call _leverage_.

## **Language Work as a Case Study**

![Photo by Compagnons on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*vcj_lKAQ0p01gaMP "Photo by Compagnons on Unsplash")

Interpretation and translation sit at an interesting intersection in this debate.

On the surface, they look vulnerable. Language processing is exactly where large AI models are strongest. Transcription, terminology, translation of standard documents: AI handles these capably and at scale.

But scratch deeper and the picture changes.

Real interpretation, the kind that matters in a courtroom, a hospital consultation, or a high-stakes cross-border negotiation, isn’t just language conversion. It’s reading the room.

Sensing when a phrase lands differently than intended. Knowing the cultural register that makes the difference between an agreement and an offence. Managing the human dynamics on both sides of a conversation in real time.

That’s not a language task. That’s a relationship task. And it’s not going anywhere.

What AI changes is the load-bearing work underneath interpretation. The preparation, the administration, the mechanical translation layer. When AI absorbs those, interpreters don’t disappear. They have more capacity for the work that was always the most important part of what they did.

This is the model Amodei is pointing at. Not AI replacing humans wholesale. AI absorbing the routine so humans can do more of the irreplaceable.

## **The Work That Gets Created**

![Photo by Resume Genius on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*aX59Jqf1DF7o9aCd "Photo by Resume Genius on Unsplash")

Here’s what the “AI will take all jobs” narrative misses: it focuses only on tasks being automated without considering new work being created.

When farming was automated, 90% of the workforce moved from farms to factories and eventually to knowledge work. The jobs that exist now didn’t exist then, but they emerged as technology created new possibilities.

AI will follow a similar pattern, just faster. Yes, entry-level knowledge work will be automated. But new categories of work will emerge from what AI makes possible.

Someone needs to prompt AI systems effectively. Someone needs to verify AI outputs. Someone needs to integrate AI into workflows. Someone needs to train others on AI use. Someone needs to handle edge cases where AI fails. Someone needs to make strategic decisions about which problems AI should solve.

These aren’t the jobs we have today. They’re the jobs that emerge when AI removes the barriers that currently limit what’s possible.

The [translation tool](https://videotranslator.ai/) I build doesn’t eliminate interpreter jobs. It makes multilingual communication possible in contexts where human interpreters were never feasible.

That creates opportunities for schools to serve international students, for healthcare providers to treat patients across language barriers, for businesses to operate globally.

That’s new work, created by removing constraints.

## **What This Means For You**

Let me be honest. AI will change your work. In fact, significantly. Amodei made that clear, and he’s in a better position than almost anyone to know.

Now the question is whether that change happens to you or with you.

The people choosing “with” aren’t naive about AI’s capabilities. They’ve just decided that understanding the tool is more useful than fearing it. They’re building habits that assume AI will handle the mechanical, which frees them to invest in everything that isn’t.

That distinction is where careers will diverge over the next five years.

And it starts with a fairly simple choice: **engage**, or **wait**.

The adolescence of technology is happening whether we participate or not. The question Sagan’s alien civilisation asked humanity still stands. How will we get through it?

The answer has less to do with the technology than with us.

AI Will Take Half of White-Collar Jobs in 5 Years

What Anthropic’s CEO wants you to do about it Half of all white-collar jobs could disappear in five years. That’s not a doom-scrolling headline from a...

On February 18th 2026, Elon Musk tweeted something that should alarm anyone who understands both AI’s capabilities and its limitations.

_“You can just take a picture of your medical data or upload the file to get a second opinion from Grok.”_

![Elon Musk continue to suggest people to upload their medical record to Grok](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/68347a1d0d3460a96eaeb31409a9bcd41c87743529f3d156cff56c23e95c48e1.avif "Elon Musk continue to suggest people to upload their medical record to Grok")

Within an hour, the post drew nearly 500,000 views. For me, it was quite shocking that the comments were full of people agreeing, sharing their own experiences using Grok for medical advice, and some even claiming today’s doctors are doing worse than an AI chatbot.

This isn’t the first time Musk has made this recommendation. He’s been encouraging people to upload X-rays, MRIs, and blood tests to Grok since October 2024.

In a June video that resurfaced in January 2026, he claimed: **_“I have seen cases where it’s actually better than what doctors tell you.”_**

@[youtube](https://www.youtube.com/watch?v=JbIO5RNFJ64)

Here’s the problem: Elon Musk has over 200 million followers. He owns the platform. He owns the AI. And he has cultivated a following that treats his statements not as opinions but as signals of where the world is heading.

When he tells people an AI chatbot can replace medical judgment, people listen. And that’s dangerous.

## **The Track Record Nobody’s Talking About**

![Photo by Salvador Rios on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*yHd-AFoUof0C1XjT "Photo by Salvador Rios on Unsplash")

Let’s examine what happens when people actually use Grok or any AI chatbot for medical advice.

Doctors who tested Grok following Musk’s 2024 invitation reported troubling failures. The AI failed to identify a “textbook case” of [tuberculosis](https://x.com/DrDatta_AIIMS/status/1851218293845295191?s=20). It misidentified a [broken clavicle](https://www.mddionline.com/artificial-intelligence/musk-goes-from-iron-man-to-dr-doom-faster-than-robert-downey-jr-) as a dislocated shoulder. In one widely reported incident, Grok mistook a breast MRI as brain MRI..

Read that again. **A breast scan. Mistaken for a brain.**

These aren’t minor errors. They’re fundamental failures in pattern recognition that any first-year medical student would catch. Yet Musk continues promoting Grok as capable of providing medical diagnoses that are “actually better than what doctors tell you.”

A May 2025 peer-reviewed study in the journal [Diagnostics](https://www.mdpi.com/2075-4418/15/11/1320#:~:text=Methods:%20ChatGPT%2D4o%2C%20Grok,during%20patient%20care%20is%20planned.) assessed ChatGPT-4o, Grok, and Google’s Gemini against 35,711 brain MRI slices. Whilst Grok performed strongest of the three in identifying pathologies, researchers noted all models showed significant limitations.

**Strongest of three** doesn’t mean r**eliable enough for medical decisions.**

It means **less terrible than the other options.**

## **The Privacy Nightmare**

Beyond accuracy issues, there’s a privacy catastrophe waiting to happen.

When you upload medical data to Grok, where does it go? X’s privacy policy states they don’t “sell your personal information,” but depending on your settings, they may share information “to certain third parties with information to help us offer or operate our products and services.”

That’s deliberately vague language. What third parties? For what purposes? Your medical data is some of the most sensitive information you possess. Uploading it to a social media platform’s AI tool means trusting that platform with information protected by medical privacy laws in clinical settings.

As [Matthew McCoy](https://www.nytimes.com/2024/11/18/well/x-grok-health-privacy.html), assistant professor of medical ethics and health policy at the University of Pennsylvania, told the New York Times: “_As an individual user, would I feel comfortable contributing health data? Absolutely not_.”

[Bradley Malin,](https://www.nytimes.com/2024/11/18/well/x-grok-health-privacy.html) professor of biomedical informatics at Vanderbilt University, added: “_This is very personal information, and you don’t exactly know what Grok is going to do with it._”

In 2024, X users were automatically opted in to having their data used to train Grok. You had to manually opt out. Now Musk is asking people to voluntarily upload medical records to that same system.

[Ireland’s Data Protection Commission](https://www.euronews.com/next/2026/02/17/ireland-launches-large-scale-probe-into-elon-musks-grok-over-ai-generated-sexual-images) launched a formal inquiry into X on 17 February 2026, examining whether X complied with GDPR obligations following reports that users had been prompted to generate non-consensual sexualised images, including of children, using Grok. This is the same platform Musk is encouraging people to trust with medical data.

## **Why People Are Falling for This**

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

The comments on Musk’s posts reveal why this message resonates. Some celebrate not needing specialists anymore. Others claim Grok helped them with post-surgery care or infected wounds.

There’s truth in that sentiment. Healthcare access is a genuine problem. Many people can’t afford specialists. A primary care consultation in the US costs $100 to $200. Grok access costs $16 monthly as part of X Premium.

The cost difference is real. The accessibility gap is real.

But the solution isn’t replacing medical expertise with an AI that mistakes breasts for a brain.

## **Where AI Actually Helps Healthcare**

I’m not anti-AI in healthcare. I build AI tools. I understand the technology’s genuine value.

AI excels at specific, well-defined tasks in clinical settings:

*   Analysing thousands of medical images to identify patterns humans might miss
    
*   Processing vast amounts of research literature to suggest treatment options
    
*   Monitoring patient vital signs for early warning indicators
    
*   Assisting radiologists by flagging areas that need closer examination
    
*   Helping researchers discover drug candidates faster
    

Notice what all these have in common? They’re tools used by medical professionals, not replacements for medical judgment.

When Google’s DeepMind developed AI for detecting diabetic retinopathy, it was deployed in clinical settings with doctor oversight. When AI helps identify potential cancers in mammograms, radiologists review the results. When AI suggests drug candidates, researchers test them rigorously.

These AI systems are trained on millions of verified medical images with expert validation. They’re tested extensively before clinical deployment. They’re used alongside human expertise, not instead of it.

Grok isn’t that. Grok is a general-purpose chatbot being crowd-tested on X users who upload their medical data for analysis.

The quality control is “_let us know where Grok gets it right or needs work,_” as Musk tweeted in October 2024.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/202141d634ef6050a84a11dcc18a5838d1bdefed5cb79e36bc447246161d854a.avif)

You’re the experiment. Your health is the test case.

## **What Actually Happens With Bad Diagnoses**

![Photo by Joshua Chehov on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*hHxajjl4XYkIHNUA "Photo by Joshua Chehov on Unsplash")

When Grok misses tuberculosis or misidentifies a fracture, real consequences follow:

A person with tuberculosis goes untreated, potentially spreading a serious infectious disease.

Someone with a broken clavicle receives wrong treatment recommendations, leading to improper healing or complications.

Someone told they have a dislocated shoulder when they have a fracture might attempt movements that worsen the injury.

Medical errors cost lives. According to [Johns Hopkins research](https://hub.jhu.edu/2016/05/03/medical-errors-third-leading-cause-of-death/), medical errors are the third leading cause of death in the United States, contributing to over 250,000 deaths annually. That’s with trained professionals making decisions.

Now imagine adding untrained AI making recommendations based on uploaded photos, interpreted by patients without medical training, who then make treatment decisions without professional guidance.

The scale of potential harm is staggering.

## **The Responsibility Musk Isn’t Taking**

When someone with 200 million followers tells people to trust an AI for medical advice, they bear responsibility for the consequences.

Musk claims he’s seen cases where Grok is “actually better than what doctors tell you.” Even if that’s true in isolated cases, it’s irresponsible to generalise from anecdotes to public health advice.

Medicine is complex. Diagnosis requires understanding symptoms in context, patient history, environmental factors, and subtle clinical signs that don’t show up in a single scan or blood test. Doctors spend years learning to interpret these factors together.

An AI trained on images can identify patterns. It can’t understand context. It doesn’t know that your back pain is related to a car accident three months ago. It doesn’t know you’re taking medications that affect blood test results. It doesn’t know your family history of heart disease.

Context matters in medicine. AI doesn’t have it.

## **What People Should Actually Do**

If you’re concerned about medical costs or want a second opinion, here are better options:

**Seek second opinions from actual doctors.** Many healthcare systems offer second opinion services. Telemedicine has made this more accessible and affordable.

**Use patient advocacy services.** Many hospitals and insurance companies provide patient advocates who can help you understand diagnoses and explore options.

**Research your condition through reputable sources.** Medical organisations like Mayo Clinic, Johns Hopkins, and NHS provide reliable patient information online.

**Ask your doctor to explain.** If you don’t understand a diagnosis, ask for clarification. Good doctors will take time to explain in terms you understand.

**Use AI as a conversation starter, not a decision maker.** If you want to understand medical terminology, fine. But take that information to a real doctor for interpretation and context.

What you shouldn’t do is upload medical data to a social media platform’s AI chatbot and base healthcare decisions on its output.

## **The Line We Shouldn’t Cross**

\*\*AI in healthcare is not the problem. AI as healthcare is the problem.  
\*\*Let this sentence sink in.

If you read Musk’s tweet and thought “that sounds reasonable,” I am not here to insult your intelligence. The idea of a second opinion is appealing. Healthcare systems are imperfect. Appointments are expensive. Wait times are brutal. The frustration is real.

But the answer to a flawed system is not to bypass it entirely with an unregulated, commercially motivated chatbot that hallucinates answers one out of every five times.

The answer is to demand better access, better funding, and better systems, while using AI to support the professionals who dedicate their lives to keeping us alive.

Musk’s recommendation is not innovation. It is recklessness dressed up as progress. And when someone with his reach says it, the stakes are not theoretical. They are measured in missed diagnoses, delayed treatments, and lives that could have been saved by a doctor who was never consulted.

Your chatbot is not your doctor. Please do not let a billionaire convince you otherwise.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/61d1e34e556162864f41435035b1f97ef838349fc8c6eb0e454c1539294a972b.avif)

## Read more:

*   [What If English is Your Company's Hidden Weakness?](https://videotranslator.ai/blog/what-if-english-is-your-companys-hidden-weakness)
    
*   [We Don't Have an AI Crisis. We Have an Accountability Crisis.](https://videotranslator.ai/blog/we-dont-have-an-ai-crisis-we-have-an-accountability-crisis)
    
*   [Should You Still Learn to Code in 2026?](https://videotranslator.ai/blog/should-you-still-learn-to-code-in-2026)

Elon Musk Says Trust Grok. It Mistook Breast for Brain

Why his medical advice is dangerously irresponsible On February 18th 2026, Elon Musk tweeted something that should alarm anyone who understands both AI’s...

Migration is, at its core, a linguistic act. 

You leave one world of words and enter another. You spend years-sometimes decades rebuilding yourself in a new language. Your accent softens. Your vocabulary expands. You start dreaming in English.

Then dementia reverses the entire journey.

For thousands of migrants across Australia, dementia doesn’t just erode memory. It erodes the language they rebuilt their lives around.

English, which is learned, practised, mastered over decades, fades. What remains is the mother tongue. The language of childhood. The one thing their Australian-born children may barely understand.

This is the double exile: first from a homeland, then from the language that replaced it.

## **The Numbers Behind The Silence**

![Photo by Mario Heller on Unsplash](https://cdn-images-1.medium.com/max/1600/0*G_qaoZKWMiJGXpe8 "Photo by Mario Heller on Unsplash")

Australia ranks among the most linguistically diverse countries on earth. Over [28 percent](https://theconversation.com/for-migrants-dementia-can-mean-losing-a-language-and-a-whole-world-263185) of Australians living with dementia come from culturally and linguistically diverse backgrounds. By 2023, an estimated 411,100 people were living with the condition nationally.

Run the numbers: that’s over 115,000 people potentially navigating dementia in a language their healthcare providers don’t speak.

Yet most dementia care infrastructure assumes English: Assessment tools. Care plans. Daily interactions. The entire system rests on a linguistic foundation that crumbles for a significant portion of the people it serves.

## **What The Brain Does and Undoes**

![Photo by Robina Weermeijer on Unsplash](https://cdn-images-1.medium.com/max/1600/0*PSHQITIR2ZIuoqHW "Photo by Robina Weermeijer on Unsplash")

Neuroscience explains the pattern clearly. Dementia erodes recently acquired skills first. Language learned in adulthood, no matter how fluen, sits in a more vulnerable cognitive layer than the mother tongue, which is encoded earlier and deeper.

The result is a cruel reversal. A man who spent fifty years as an English-speaking engineer in Perth wakes up speaking only Italian. His wife, also Italian-born, might still understand. His children, raised in English-speaking schools, might not. His doctor almost certainly doesn’t.

Suddenly, the most basic acts of care like explaining pain, understanding medication instructions, expressing fear require translation. And translation requires resources that are perpetually scarce.

## **The Interpreter Gap**

![Photo by Age Cymru on Unsplash](https://cdn-images-1.medium.com/max/1600/0*IOqBuJ05X36YBADi "Photo by Age Cymru on Unsplash")

Professional interpreters remain the gold standard for cross-language healthcare. But the reality falls short of the ideal.

Dementia-trained interpreters are rare. Scheduling takes time that acute care situations don’t allow. And interpreters can’t be present for the hundreds of small daily interactions that constitute life with dementia: the morning greeting from a nurse, the confused request at 4 a.m., the attempt to describe a headache to a care aide.

These micro-moments matter. Research shows that language-sensitive dementia care reduces patient distress, improves diagnostic accuracy, and lowers caregiver burnout. Every interaction that falls through the language gap is a missed opportunity for connection and care.

## **How Can We Solve This?**

This is the space where real-time AI conversation tools make the biggest difference. Not replacing interpreters, but covering the vast territory between scheduled appointments.

[VideoTranslatorAI’s](https://videotranslator.ai/) real-time conversation tool interprets live speech across 120+ languages and summarises key points as the conversation unfolds. No downloads, no setup beyond a browser and internet connection.

![Illustration: How VideoTranslatorAI's interpreter works.](https://cdn-images-1.medium.com/max/1600/1*b-u1DCrSBeD0Oq-Of2-6Qw.png "Illustration: How VideoTranslatorAI's interpreter works.")

Illustration: How VideoTranslatorAI’s interpreter works.

In practice, this means a care worker can conduct a bedside check-in with a patient who now speaks only Cantonese and understand them in real time.

A family member joining a specialist consultation can follow along even when their parent switches to a language they barely know.

After the conversation ends, an AI-generated summary captures what was discussed: symptoms reported, concerns raised, and instructions given.

For overstretched care facilities, this isn’t a luxury. It’s infrastructure.

![](https://cdn-images-1.medium.com/max/1600/1*DDP18GFUFXS2f6s_MlFE3g.gif)

## **From Add-on to Foundation**

Australia has bright spots. Dementia Support Australia provides translated materials and arranges interpreter services. 

Victoria’s “language buddies” program pairs isolated individuals with volunteers who share their language. Bilingual cognitive assessments grow more common each year.

But these efforts still treat linguistic diversity as a special accommodation, something bolted onto an English-first system. The shift that’s needed is foundational: designing care systems that _expect_ multilingual patients from the start.

Real-time interpretation and summarisation tools make this practical. When multilingual support is instant and embedded (not booked, scheduled, and budgeted separately), it stops being an exception and starts being the default.

### The Words That Remain

![Photo by Georg Arthur Pflueger on Unsplash](https://cdn-images-1.medium.com/max/1600/0*mKB2zWKFn4k5gRJ_ "Photo by Georg Arthur Pflueger on Unsplash")

Here is what makes this story both heartbreaking and strangely beautiful: the language dementia preserved is the most intimate one. The first words a person ever learned. The sounds of parents, siblings, childhood streets.

The loss isn’t that someone reverts to their mother tongue. The loss is that the world they live in now can’t meet them there.

Closing that gap with trained interpreters, culturally informed care, and technology that works in real time isn’t just good policy. It’s basic human decency.

No one should face dementia in a language the people around them can’t understand.

## **Read more:**

*   [What If English is Your Company's Hidden Weakness?](https://videotranslator.ai/blog/what-if-english-is-your-companys-hidden-weakness)
    
*   [We Don't Have an AI Crisis. We Have an Accountability Crisis.](https://videotranslator.ai/blog/we-dont-have-an-ai-crisis-we-have-an-accountability-crisis)
    
*   [Should You Still Learn to Code in 2026?](https://videotranslator.ai/blog/should-you-still-learn-to-code-in-2026)

115.000 Elderly Migrants Lose English to Dementia

The double exile facing 115,000 Australian elderly migrants Migration is, at its core, a linguistic act. You leave one world of words and enter another.

They Built a Life in English. Dementia Undid It.

If someone handed you a blank page and asked you to write down the one thing the world needs most, what would you put?

Not a manifesto. Not a policy proposal. Not a strategic roadmap.

One word.

It sounds like a simple exercise, but try answering it honestly.

Love? Peace? Innovation? Equality?

Each feels right for a moment before it buckles under the weight of everything it leaves out.

Love does not solve supply chains. Peace does not allocate resources. Innovation without direction simply creates faster problems.

I sat with this question for a long time. Longer than I expected. And the word that kept surfacing was not the one I thought it would be.

> W**isdom**.

Not intelligence. Not innovation. Not even compassion, though the world could use more of that too. **Wisdom**.

## **We Have Never Been More Capable**

![Photo by Marília Castelli on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*gj2p9Om_NO31Fy94 "Photo by Marília Castelli on Unsplash")

Consider what we have built.

A teenager in India can access more knowledge in a single afternoon than a scholar in the 1800s could gather across an entire lifetime. Machines now write poetry, create wonderful paintings, argue court cases, and compose symphonies.

We carry supercomputers in our pockets. We sequence genomes before lunch. We land reusable rockets on drone ships and barely stop scrolling long enough to notice. Every week delivers a breakthrough that would have seemed like science fiction a generation ago.

By almost every measurable standard, humanity has never been more capable. We have more tools, more data, more processing power, and more connectivity than any civilisation in history. The pace of progress is not just fast. It is accelerating.

And yet. Look around.

We are more anxious, more divided, and more uncertain about the future than most generations before us.

We have the tools to address climate change, but lack the collective will to deploy them.

We have the medical knowledge to save millions, but cannot reach a consensus on how to distribute it.

We have technologies powerful enough to free humanity from drudgery, yet we spend more time debating who they will replace than imagining what we could become.

The gap between what we _can_ do and what we _actually_ do well is not shrinking. It is growing. Every breakthrough widens it further.

Capability is not the bottleneck. Direction is.

And direction is what wisdom provides.

## **The Quiet Crisis**

![Photo by Pars Sahin on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*4Fp8eqACIVkQeP_b "Photo by Pars Sahin on Unsplash")

Here is the part no one wants to acknowledge.

We have become extraordinarily good at doing things. Faster, cheaper, at greater scale than any generation in history.

But somewhere along the way, **we stopped asking whether we should.**

We optimised for _speed_ and forgot to check where we were heading. We built systems _more intelligent than us_ without first becoming wiser ourselves.

We celebrated _disruption_ without pausing to ask what we were disrupting, or whether what we tore down deserved to stand.

We worshipped _efficiency_ without questioning what we were being efficient at.

We chased _growth_ quarter after quarter without facing the uncomfortable question: growth towards what?

We built _social networks_ to connect people and watched them fracture communities.

We created _attention economies_ and then wondered why no one can focus.

We automated decision-making and lost track of who was actually deciding.

**These are not failures of intelligence. They are failures of wisdom.**

Capability without wisdom is just a faster way to make bigger mistakes.

Consider the tools at our fingertips right now. We can split atoms and edit genes. We can generate synthetic voices indistinguishable from real human speech. We can blanket the internet with content at the press of a button. We can surveil populations, shape public opinion at scale, and automate decisions that reshape millions of lives without those lives ever being consulted.

But ask us _why_ we are doing any of it, and the room falls silent.

That silence is the crisis. Not a loud, headline-grabbing crisis with emergency summits and breaking news tickers. A quiet one. The kind that creeps in while everyone is busy optimising something they never stopped to question.

## **Knowledge is Not Wisdom**

![Photo by ThisisEngineering on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*6kLde2gn65iFD-6h "Photo by ThisisEngineering on Unsplash")

The distinction matters more than most people realise.

Knowledge tells you that a fire burns. Wisdom tells you when to light one and when to walk away.

Knowledge can be stored on a server, compressed into a file, indexed, and retrieved in milliseconds by anyone with an internet connection.

Wisdom cannot. Wisdom lives in the slow, often painful process of being human. In failure and what follows it. In empathy that costs you something real. In sitting with a difficult question long enough to hear an honest answer, rather than grabbing at a convenient one.

Knowledge is accumulation. Wisdom is discernment.

Knowledge gives you options. Wisdom helps you choose between them. Knowledge can be downloaded. Wisdom has to be earned, and it cannot be rushed, shortcut, or hacked.

You can hold multiple degrees and lack wisdom entirely. You can have no formal education and carry more wisdom than a roomful of credentialed experts.

Wisdom does not care about your CV or your citation count. It cares about whether you have done the unglamorous, deeply personal work of learning from your own life: from your mistakes, your quiet doubts, the choices that kept you up at three in the morning and the slow mornings that followed.

This is precisely why **artificial intelligence, for all its brilliance, cannot replicate wisdom**.

Not because machines lack data or processing power. But because wisdom demands something data cannot produce on its own: the lived experience of making hard choices and sitting with their consequences. The weight of regret. The patience of recovery. The gradual sharpening of judgement that only comes from being a person in the world, getting things wrong, and paying close attention to what the getting-wrong teaches you.

## **The Questions Only Wisdom Can Answer**

![Photo by Sergio Arze on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Ic2sizeQetibdCyj "Photo by Sergio Arze on Unsplash")

What the world needs is not smarter machines, faster networks, or more disruptive innovations.

The world needs people who can stand in front of all these extraordinary tools and ask a different set of questions entirely.

> What are they actually for? Who do they serve? What kind of world are we building with them? And do we actually want to live in it?

Those are not technical questions. They are human ones. And only wisdom can answer them.

We need leaders who can sit with uncertainty instead of manufacturing false confidence to fill the silence.

We need builders and technologists who ask **_“should we?”_** with the same intensity they bring to **_“can we?”_**

We need educators who teach young people not just what to think, but how to think well.

We need communities that prize depth over speed, reflection over reaction, and long-term flourishing over next quarter’s numbers.

None of that comes from more information. We already have more information than we know what to do with.

Our feeds overflow. Our inboxes groan. Our attention fractures across an endless scroll of input. What we lack is not more data. It is the maturity, the patience, and the presence to use what we already have well.

That is what wisdom is. Not a retreat from progress. Not a rejection of technology or a nostalgic yearning for simpler times.

Wisdom is the compass that tells you whether your extraordinary capabilities are pointed somewhere worth going. It is the pause before the action. The question before the answer. The human in the loop who still remembers what the loop is actually for.

## **Your Turn**

![Photo by Enchanted Tools on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*bDka_sYCivC06t3t "Photo by Enchanted Tools on Unsplash")

I have given you my word. One word for what I believe the world needs above all else.

> Wisdom.

But this is not a lecture. It is an invitation.

If you had to choose one word to describe what the world needs most, what would it be? Not a paragraph. Not an argument. Not a debate. One word.

Sit with it before you respond. Let the noise settle. Let the first few obvious answers drift through and keep going. Because the word you choose reveals something honest: not only about the world as you see it, but about the future you are quietly building towards.

And perhaps choosing that word carefully, honestly, without rushing to be clever, is itself a small act of wisdom.

Your Intelligence Is Useless Without This

And actually, it’s the one thing that the world needs the most right now. If someone handed you a blank page and asked you to write down the one thing the...

Let’s run an experiment.

Picture your last international call. Now count the moments someone hesitated, rephrased, or said “how do you say…” before trailing off.

Those pauses have a price tag: **£426,000 annually** for the average London business, according to new research from DeepL. That’s £90,000 more than the national average.

But here’s what the number doesn’t capture: the insight that got simplified for easier translation. The objection that felt too complex to articulate. The creative solution that stayed locked inside someone’s head because the meeting language wasn’t their thinking language.

This translation tax isn’t a communication problem. It’s an intelligence leak.

## **The Silent Tax You’re Already Paying**

![Photo by Kelly Sikkema on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/63c09c9bf97c43db547eb45103874e8f547f1c9f92abce309021c6c37ece8b24.jpeg "Photo by Kelly Sikkema on Unsplash")

Here’s where it gets uncomfortable.

**58% of London professionals** believe the UK is falling behind other economies because of poor language accessibility. Not infrastructure. Not policy. _Language._

The irony cuts deep: English speakers built an entire global business culture around their native tongue, then wondered why cross-border deals keep falling apart.

**53% of business leaders** admit language differences have limited their international growth. Not “slightly inconvenienced” but _limited_. Deals die in translation. Partnerships stall over misunderstood nuance. Entire markets remain inaccessible because nobody in the room can truly hear what the other side is saying.

Inside organisations, the friction compounds. **54% of workers** report that language barriers create tension between global teams. **41%** say their companies don’t encourage speaking other languages internally.

The message beneath the message: _Just speak English. We’ll figure it out._

Except we’re not figuring it out. We’re haemorrhaging money and trust.

## **The Myth That’s Costing You Millions**

![Photo by sarah b on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/86b6d22c68e446130fd8cb48606e3bb76b36e75876610119266ae283b5939052.jpeg "Photo by sarah b on Unsplash")

For decades, “English is enough” felt like reasonable business logic.

That logic is collapsing.

**42% of Londoners** now consider English-as-default to be outdated thinking. And here’s the statistic that should keep monolingual executives awake: **only** **20% of the global population** speaks English fluently.

You’re not reaching “the world” when you communicate in English. You’re reaching one-fifth of it — and even then, you’re asking 80% of your potential partners, clients, and colleagues to perform mental gymnastics just to participate.

**77% of London workers** have felt embarrassed when colleagues abroad default to English. **53%** regret not studying more languages earlier.

The embarrassment isn’t guilt. It’s recognition. We’re beginning to understand that linguistic privilege has a cost, and everyone pays it.

## **What Changes When Everyone Gets Heard**

The fix isn’t learning Mandarin over the weekend.

It’s building systems where language stops being a filter for who gets to contribute.

At [VideoTranslatorAI](https://videotranslator.ai/), we built a meeting agent that eliminates the pause, the search for words, the polite smile that masks a lost point.

![](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/722aaaea7b6fd89c483167339c915daba98848cb5ed5bfa4386d81201d88718f.gif)

**_(Real-time meeting translation by_** [**_VideoTranslatorAI)_**](https://agent.videotranslator.ai/)

Our AI transcribes conversations in real-time, interprets across over 50 languages instantly, and summarises key decisions so nothing falls through the cracks.

Think about what that unlocks:

*   The German executive finishes her own thought accurately
    
*   Your Tokyo team contributes insights without waiting for translation lag
    
*   Post-meeting summaries capture what was _actually_ said, not what got filtered through linguistic bottlenecks
    

This isn’t about replacing human connection. It’s about removing the barriers that prevent it.

## **From Tax to Dividend**

![Photo by Amina Atar on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/be18e1993ecdfb6a2c868bf02f7182d98ad32747236a8108022898656bba6226.jpeg "Photo by Amina Atar on Unsplash")

Language accessibility isn’t a line item. It’s infrastructure.

Companies investing in multilingual communication aren’t just avoiding the Translation Tax — they’re building something more valuable: **trust at scale.**

When people speak in their native language and know they’ll be understood, everything shifts. Negotiations get honest. Feedback gets specific. Relationships move past polite and into productive.

**68% of London companies** are already adjusting hiring strategies around language needs. **95%** factor language skills into recruitment.

The shift is happening. The question is whether you’re leading it or catching up.

## **The Real Question**

London’s Translation Tax isn’t really about money.

It’s about who gets heard. Who gets to bring their full intelligence to the table. Who shapes decisions versus who just nods along.

Your next meeting will include people thinking in languages that aren’t English. The only question: will you hear what they’re actually trying to say?

Stop paying the Translation Tax. Start building meetings where everyone contributes.

_Source:_ [_London Daily News, 2025_](https://www.londondaily.news/language-barriers-impacting-london-businesses-according-to-study-from-deepl/)

## **Read more:**

*   [We Don't Have an AI Crisis. We Have an Accountability Crisis.](https://videotranslator.ai/blog/we-dont-have-an-ai-crisis-we-have-an-accountability-crisis)
    
*   [Should You Still Learn to Code in 2026?](https://videotranslator.ai/blog/should-you-still-learn-to-code-in-2026)
    
*   [95% of AI Pilots Fail. Voice AI Explains Why.](https://videotranslator.ai/blog/95-percent-ai-pilots-fail-voice-ai-explains-why)

What If English is Your Company's Hidden Weakness?

The cost of forcing everyone to think in one language and speak in another Let’s run an experiment. Picture your last international call.

I was scrolling through Twitter before heading to bed when a tweet caught my eye, and that I just can’t shake off:

![@RaminNasibov](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/c003481a35e63063a1a591aae6faa5c166b86861cc88c3625d52ee18ae88e5bc.png "@RaminNasibov")

Source: [@RaminNasibov](https://x.com/RaminNasibov/status/2021176941819170973) on X

My gut answered before my brain caught up.

Yes. It’s the people. It’s always been the people.

We’ve spent years building a boogeyman out of algorithms and neural networks. Headlines scream about AI taking jobs, AI spreading lies, AI ending humanity.

But strip away the fear and look at what’s actually happening. In every case, a human made a choice. The AI just followed instructions.

We’re prosecuting the weapon while the criminal walks free.

## **A Tale of Two Wizards**

![Illustration of a good witch vs. wicked witch — created with Canva AI. ](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/60e77c846c3a26d92276fd1863478a1969a91777539ad2ebd8a384a324486133.png "Illustration of a good witch vs. wicked witch — created with Canva AI. ")

I always think of AI as a magic wand, a _tool_ of immense power that amplifies whatever the wielder intends.

Now imagine there are two wizards standing before identical wands, each crackling with the same raw power.

The first wizard raises her wand and heals. She mends broken bones, restores sight to the blind, conjures shields that protect villages from storms. When plague sweeps through a distant kingdom, she creates potions that cure thousands. Her magic flows outward, touching lives she’ll never meet, solving problems she’ll never see firsthand. The wand amplifies every generous impulse in her heart.

The second wizard raises her wand and destroys. She curses strangers, sows discord between neighbours, conjures illusions that deceive the vulnerable into ruin. When she sees power, she wants more. When she sees trust, she exploits it. The same wand that healed in one set of hands now corrupts in another.

**Same wand. Same magic. Different hearts.**

![Photo by Air Melody on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/3961c9974c1c02ba66e38d4b2b689c731aa81c7e641129adaa66cf54805fc5be.jpeg "Photo by Air Melody on Unsplash")

Now replace “wand” with “artificial intelligence.” Replace “wizard” with “builder.”

The metaphor collapses into reality. Same underlying technology. Same machine learning models. Same artificial intelligence.

The AI didn’t decide which path to take. The humans did.

## **The Good Wizards Among Us**

![Photo by Harpreet Singh on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/4416d6572083e84bf3ef458e8df21ababf38c2eccf47fbdd01aa8697e048e6ae.jpeg "Photo by Harpreet Singh on Unsplash")

That grammar checker suggesting clearer phrasing for your important email? AI making your communication better.

The recommendation that led you to your favourite song last year? AI understanding something about you that even you couldn’t articulate.

The fraud detection that blocked a suspicious transaction before someone emptied your bank account? AI protecting you while you slept.

Medical imaging analysis now catches tumours that human eyes miss. Not sometimes. Regularly. Thousands of people alive today who would have received their diagnosis too late.

Agricultural AI helps farmers in developing nations optimise water usage during droughts. Children eat because machine learning analysed satellite imagery and soil samples.

Accessibility tools powered by AI let blind people “see” photos their friends share, describe scenes in videos, navigate streets independently.

This is the technology being cast as humanity’s great threat.

## **The Dark Wizards are Real Too**

![Photo by JOE Planas on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/21eb3ac050eea08e904e63b8c7008a17b67ff0aeb94c9ed7fdc722fba4284ed5.jpeg "Photo by JOE Planas on Unsplash")

I won’t pretend the dark side doesn’t exist. It absolutely does.

Voice cloning has become terrifyingly accurate. Scammers need mere seconds of audio to replicate someone’s voice convincingly.

Parents have received ransom calls from their “kidnapped” children. The children were fine, sitting in school. But those minutes of terror were real.

Misinformation now generates itself. AI creates fake news articles, fake expert quotes, fake evidence for claims that would crumble under any scrutiny.

People believe it because it looks legitimate. Democracy suffers when truth becomes indistinguishable from fabrication.

Job displacement isn’t hypothetical anymore. Companies eliminate positions that AI can do cheaper. Workers with decades of experience find themselves competing against systems that don’t need sleep, benefits, or dignity.

And yes, weapons. Autonomous systems that could theoretically select and engage targets without human decision. The technology exists. The ethical framework for deploying it does not.

But here’s what I need you to notice: **every single one of these harms required a human decision.**

_Someone chose to use voice cloning for scams instead of helping stroke survivors speak again._  
_Someone chose to generate misinformation instead of educational content._  
_Someone chose to eliminate jobs without transition support._  
_Someone chose to develop lethal autonomy instead of search-and-rescue drones._

**Remove the malicious choice, and the technology becomes neutral or even beneficial.**

## **The Question We Should Actually Ask**

![Photo by Abu Saeid on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/996189cc6bcd4d62ca320914dbe9c60d3dc347a4e0947e4e3d5227bc3823d649.jpeg "Photo by Abu Saeid on Unsplash")

This brings us to the uncomfortable part.

If AI is just a tool, and tools depend on who wields them, then the problem we face isn’t technological. It’s human. It always has been.

We don’t have an AI crisis. We have an accountability crisis.

As someone who builds AI tools, I ask myself some questions before every feature or capability:

**_What’s the worst this could enable?   
Who might misuse this?   
Is the potential good worth the potential harm?_**

Sometimes the answer is no. And that’s the point.

The reflection isn’t about whether AI is inherently good or evil. It’s neutral. The reflection is about whether we, as builders and users, have the wisdom to wield it responsibly.

We don’t ban cars because drunk drivers kill people. We create laws, enforce consequences, and trust that most humans will drive responsibly.

We don’t prohibit kitchen knives because they can become weapons. We hold the attacker accountable, not the cutlery.

Why do we treat AI differently?

## **Stop Blaming the Mirror**

![Photo by Михаил Секацкий on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d7687e1a12b249feb779906454da35f5f63fe68141d4b655a09df0b397ff77dd.jpeg "Photo by Михаил Секацкий on Unsplash")

Here’s my take.

> “AI scares us because it shows us ourselves.”

When we see AI generating misinformation, we’re really seeing human appetite for lies that confirm our biases.

When we see AI enabling surveillance, we’re witnessing human hunger for control over others.

When we see AI automating jobs away, we’re confronting human systems that value profit over people.

AI didn’t create these impulses. It amplified what already existed within us.

That’s uncomfortable. It’s much easier to point at the technology and say, “That’s the villain.” But comfort doesn’t solve problems. Honesty does.

The fear isn’t actually about artificial intelligence. It’s about human nature armed with increasingly powerful tools.

## **Where Do We Go From Here?**

![Photo by Vitaly Gariev on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/869c7bd6838942d7c44bebf8eba6fde579040f90e4d2a36a8dd24fb826fb8a3b.jpeg "Photo by Vitaly Gariev on Unsplash")

So what do we do with this realisation?

**First**, we stop the lazy narrative. “AI is dangerous” is incomplete. “AI in malicious hands is dangerous” tells the full story. Language matters. How we frame threats shapes how we address them.

**Second**, we regulate the wizards, not just the wands. Laws that punish deepfake creators more harshly than the platforms hosting them. Policies that hold scammers accountable rather than just blocking their tools. Frameworks that ensure human oversight in high-stakes AI applications.

**Third**, we build responsibly. Those of us creating AI tools must ask the hard questions before shipping features. We must design with misuse in mind. We must sometimes choose not to build something, even when we can.

**Fourth**, we educate. When people understand how AI actually works, they fear it less irrationally and guard against its misuse more effectively. Demystification is protection.

And **finally**, we remember that this isn’t new. Every powerful technology in human history has been used for both miracles and atrocities. Fire warmed our ancestors and burned their enemies. The printing press spread enlightenment and propaganda. The internet connected humanity and enabled its worst corners.

AI is simply the latest chapter in humanity’s oldest story: powerful tools amplifying whoever holds them.

The wand has never been evil. The wand has never been good.

The wand simply is.

What matters, what has always mattered, is the heart of the wizard who picks it up.

So the next time someone asks whether we should fear AI, I’ll give them the same answer:

> **Fear the human who wants to harm you with it. Celebrate the human who wants to help you with it. And if you ever hold that wand yourself, choose carefully what you conjure.**

The spell is yours to cast.

## Read more:

*   [Should You Still Learn to Code in 2026?](https://videotranslator.ai/blog/should-you-still-learn-to-code-in-2026)
    
*   [95% of AI Pilots Fail. Voice AI Explains Why.](https://videotranslator.ai/blog/95-percent-ai-pilots-fail-voice-ai-explains-why)
    
*   ["Yes, Okay, Thank You" and Your Child Just Failed Another Grade](https://videotranslator.ai/blog/yes-okay-thank-you-and-your-child-just-failed-another-grade)

We Don't Have an AI Crisis. We Have an Accountability Crisis.

A call to stop blaming AI for human decisions I was scrolling through Twitter before heading to bed when a tweet caught my eye, and that I just can’t shake...

The most powerful artificial intelligence companies on Earth are hiring for soft skills. They’re advocating for humanities degrees. They’re telling people that creativity matters more than coding.

If this sounds backwards, you’re not alone. But when the architects of AI unanimously point toward human capabilities as the skills of the future, it’s worth paying attention.

In a recent [OpenAI town hall](https://www.youtube.com/watch?v=Wpxv-8nG8ec&t=2348s), Sam Altman was asked what skills people should develop as AI transforms every industry. His answer caught many off guard.

> _“Become high agency,” he said. “Get good at generating ideas. Be very resilient. Be very adaptable to a rapidly changing world. These are going to matter more than any specific skill.”_

Not coding. Not prompt engineering. Not technical expertise.

**Agency. Ideas. Resilience. Adaptability.**

This advice becomes even more striking when you realise it’s coming from multiple leaders across the AI industry. From OpenAI to NVIDIA to Anthropic to Microsoft, the founders shaping our technological future are pointing toward the same unexpected conclusion.

## **The Death of Raw Intelligence as Currency**

For decades, we’ve organised education and careers around a simple premise: accumulate knowledge, demonstrate expertise, climb the ladder. But Altman argues this model is becoming obsolete.

“There will be a kind of ability we still really value, but it will not be raw, intellectual horsepower,” he explained in a [podcast](https://www.ted.com/talks/rethinking_with_adam_grant_sam_altman_on_the_future_of_ai_and_humanity) with Adam Grant.

The new premium ability? **Learning how to ask great questions.**

Think about that shift. In a world where AI can access and synthesise virtually all human knowledge in seconds, memorising facts or knowing where to find them loses its value.

What remains valuable is the uniquely human capacity to frame problems, to ask questions nobody else is asking, to see connections others miss.

Altman put it bluntly when reflecting on future generations: “My kid is never gonna grow up being smarter than AI. And that’ll be natural.”

The question isn’t whether AI will surpass human intelligence in specific domains. It already has. The question is what humans will do that AI cannot.

## **Domain Expertise Meets AI Collaboration**

![Photo by National Institute of Allergy and Infectious Diseases on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/f6475086542519e369a0e47959a314d20a01ef0996675cc4f17ec4279918d7ee.jpeg "Photo by National Institute of Allergy and Infectious Diseases on Unsplash")

At the [Cisco AI Summit](https://www.youtube.com/watch?v=6fbyiPRhMSs) held earlier this month, NVIDIA CEO Jensen Huang offered a complementary perspective.

The man whose company powers most of the world’s AI infrastructure thinks that success in the AI era is no longer about coding, which, he believes, has become a commodity.

_“All of you have something of great value, which is_ **_domain expertise_**_, to understand the customer, understand the problem. And that is the ultimate value,”_ Huang emphasised.

This reframes the entire conversation. Rather than viewing AI as something to master technically, Huang sees it as something to **collaborate** with.

Your unique domain knowledge, whether in medicine, law, education, agriculture or any other field, becomes more valuable when amplified by AI.

Huang also gives us a specific recommendation: treat AI like your smartest colleague.

Learn to work alongside it, to prompt it effectively, to blend your expertise with its capabilities. _“It is vital that we upskill everyone, and the upskilling process, I believe, will be delightful. Surprising,”_ as he stated in last year’s [World Government Summit.](https://www.youtube.com/watch?v=8Pm2xEViNIo&t=430s)

The smartest people, according to Huang, aren’t necessarily the most technical, but those who are able to see around corners.

Foresight, intuition, strategic thinking. These human qualities become differentiators precisely because AI excels at processing what already exists, not imagining what could be.

## **The Humanities Revival**

![Photo by Christina @ wocintechchat.com M on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/3f893f8431d7c73ac2f6ce73a6846579e04f292e11e16338295ae0a7481752fe.jpeg "Photo by Christina @ wocintechchat.com M on Unsplash")

Perhaps the most counterintuitive advice comes from [Daniela Amodei,](https://finance.yahoo.com/news/anthropic-cofounder-says-studying-humanities-171902740.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAMuZgLyagkGr8H1_x04Wt_wLuBIH9k0BO6l2SDHuD0nDoNn9dVOsxTPiNjfmDk6bFLB27DLrHOmNe1htwfnebZN_dMXNRR2sgl4xIrMkAhGffpfHcLq_KWTg5kYuIOkWkgfCavKXX-8-q3RsT7RdHgH3SJOJG1ugIegXaMc81Jy7) co-founder and president of Anthropic, the company behind Claude AI.

In a world racing to build artificial general intelligence, Amodei argues that studying the humanities will be **_“more important than ever.”_**

When Anthropic hires, they actively seek soft skills: communication, ethical reasoning, the ability to understand human context and nuance.

Why? Because AI excels at processing information but struggles with human context, ethical reasoning and nuanced communication.

Technical skills can be automated or augmented. But understanding human motivation, navigating complex social dynamics, exercising moral judgment in ambiguous situations: these capabilities remain stubbornly human.

The humanities teach exactly these skills. Literature develops empathy. Philosophy sharpens ethical reasoning. History provides pattern recognition across human behaviour.

In an age of artificial intelligence, these “soft” skills may prove to be the hardest to replace.

## **Three Adaptation Strategies**

![Photo by Simon Ray on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/b3da73d60718707ae756354cdcf67bce2b27a5115096725f24b257067cf8dc1d.jpeg "Photo by Simon Ray on Unsplash")

Synthesising advice from AI’s architects, three strategies emerge in my mind:

### **Strategy 1: Develop meta-skills over specific skills**

Specific skills become outdated. Meta-skills, the ability to learn, adapt and transfer knowledge, remain valuable regardless of what changes.

Altman’s emphasis on agency and resilience reflects this. These aren’t skills you deploy; they’re capacities that enable you to develop whatever skills become necessary.

### **Strategy 2: Go deep, then collaborate**

Huang’s advice suggests a two-step process. First, develop genuine expertise in a domain you care about. Then, learn to collaborate with AI to amplify that expertise.

Shallow knowledge of many things may matter less than deep understanding of something specific, combined with the ability to leverage AI for everything else.

### **Strategy 3: Invest in irreplaceable human qualities**

Communication, empathy, ethical reasoning, creativity, judgment: these capabilities remain stubbornly human. As technical tasks become automated, they become comparatively more valuable.

Amodei’s advocacy for humanities education reflects this. The “soft” skills may be the hardest to replicate, making them the safest long-term investment.

## **A Different Kind of Race**

![Photo by Enchanted Tools on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d164d7512017ecdb6f39cc3db1f4f3639d9705ea825df838863de207d788c1e9.jpeg "Photo by Enchanted Tools on Unsplash")

There’s a race underway, but it may not be the race we imagined.

It’s not humans versus machines. The founders building AI don’t describe competition; they describe collaboration.

It’s not technical skills versus soft skills. Both matter, but soft skills may have more lasting value.

The real race is against stagnation. Against the temptation to assume current skills will remain sufficient. Against the comfort of expertise that resists change.

## **Moving Forward**

The path forward isn’t about predicting exactly which skills will matter in 2030 or 2040. Predictions that specific are impossible.

It’s about developing the capacity to adapt regardless of what emerges. Agency. Resilience. Continuous learning. Deep expertise combined with collaborative flexibility. Human capabilities that machines cannot replicate.

These aren’t things you learn from a textbook. They’re capabilities you develop by doing hard things, failing, learning, and trying again.

Practice asking great questions. Not just good questions, but the questions that reveal hidden assumptions, clarify confusion, and open new possibilities.

The founders building AI are offering a warning and an invitation. The warning: change is coming faster than most people expect. The invitation: the future favours those who develop distinctly human strengths.

The question isn’t whether you can keep up with AI.

**_It’s whether you can keep growing alongside it._**

## **Read more:**

*   [**The Teletubbies' Vacuum is Better Engineering Than Tesla's Optimus**](https://videotranslator.ai/blog/teletubbies-vacuum-is-better-engineering-than-tesla-optimus)
    
*   [**"Yes, Okay, Thank You" and Your Child Just Failed Another Grade**](https://videotranslator.ai/blog/yes-okay-thank-you-and-your-child-just-failed-another-grade)
    
*   [**95% of AI Pilots Fail. Voice AI Explains Why.**](https://videotranslator.ai/blog/95-percent-ai-pilots-fail-voice-ai-explains-why)

Here’s what OpenAI, NVIDIA, and Anthropic say you need to survive the age of AI The most powerful artificial intelligence companies on Earth are hiring for...

Should You Still Learn to Code in 2026?

A company just launched an AI pilot. The demo was impressive. The executives are excited. The budget is approved.

Six months later, it’s quietly shelved. No measurable impact. No adoption. No one wants to talk about it.

If this sounds familiar, you’re not alone. An MIT study analysing over 300 enterprise AI initiatives found that [95%](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/) of AI pilots fail to deliver any measurable return on investment.

The number is almost too brutal to believe. But the problem isn’t the technology. It’s how organisations adopt it.

And nothing reveals this gap more brutally than **_voice AI_**.

Here’s my brief explanation.

## **Why Voice AI Is the Hardest Test**

![Photo by Egor Komarov on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/b7effc97803e13b6fe19da04cadfa8611956b37572170909533dc855259a1608.jpeg "Photo by Egor Komarov on Unsplash")

Text-based AI has a crucial advantage: forgiveness.

When ChatGPT makes a mistake in an email draft, you can edit it. When a document summarisation tool misses context, you can add it manually. Text AI operates in a forgiving environment where humans can catch errors before they cause damage.

Voice AI doesn’t get that luxury.

In live conversations, mistakes happen in real time. A second of lag during a client call. A mistranslated phrase in a medical consultation. The wrong tone in a customer service interaction. These errors don’t just sit in a draft waiting for correction. They immediately erode trust.

That’s why voice AI is the real test of organisational readiness. It forces companies to confront the hard truths they can ignore with text AI: brittle workflows, poor integration, misaligned expectations, and lack of proper governance.

## **What Voice Reveals**

![Photo by Bluestonex on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/3104ec564e4bb5abb64c6f85adce413e062a1a72286eee89b26fecab811a4da6.jpeg "Photo by Bluestonex on Unsplash")

Voice AI acts like a stress test for organisational infrastructure. When it fails, the failure almost never traces back to the model.

It traces back to workflows that were never designed for real-time accountability:

**Compliance processes** built on institutional knowledge that nobody documented. The AI can’t follow rules that only exist in someone’s head.

**Sales playbooks** that vary by rep, by region, by whoever happened to train the new hire. The AI can’t support a process that doesn’t exist consistently.

**Meeting cultures** where decisions are made but never captured, where action items are announced but never tracked, where “we discussed this” becomes an alibi instead of a commitment.

Text-based tools let these problems persist. Voice AI forces them into the open.

## **What Successful Adoption Actually Looks Like**

![Photo by Jo Lin on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/483428a6ab699c66287d95f0d32ba43606ab1f8b9edc571f816697d9c921eb84.jpeg "Photo by Jo Lin on Unsplash")

The companies that make voice AI work don’t have better technology. They have better organisational clarity.

They embed AI into workflows rather than running it alongside them:

**Customer support** teams use real-time transcription and multilingual interpretation to serve global clients without manual translation delays. The conversation stays fluid. Trust compounds.

**Meeting-heavy organisations** capture decisions, action items, and commitments as they happen — not as someone reconstructs them days later. Accountability becomes automatic, not aspirational.

**Compliance functions** generate audit-ready documentation without the 10+ hours of weekly manual reporting that burns out staff and introduces human error.

The pattern is consistent: voice AI succeeds when it’s designed into how people already work, not added on top.

## **The Generational Tension You’re Probably Ignoring**

![Photo by Eliott Reyna on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d0dbaa16672df8cab2cdb72df41d555e8056219332915b4ef8a168eada9d0e04.jpeg "Photo by Eliott Reyna on Unsplash")

There’s a friction point most organisations underestimate: younger workers expect AI to reduce friction, while senior professionals often experience it as surveillance.

Gen Z employees treat AI assistance like autocomplete — natural, helpful, invisible when it works. They don’t feel monitored. They feel supported.

Experienced professionals who built their careers on judgment and expertise can feel differently. A machine transcribing their words in real-time can read like distrust. An AI summarising their meeting can feel like their assessment wasn’t good enough.

Successful voice AI adoption addresses this head-on. The framing matters: augmentation, not replacement. Enhancement of human judgment, not substitution for it.

When both groups trust the tool, adoption accelerates. When they don’t, it stalls — no matter how good the technology is.

## **Building for Real-Time Trust**

At [VideoTranslatorAI](https://videotranslator.ai/), we built a meeting assistant that transcribes, interprets, and summarises conversations in real time because we watched organisations fail to capture what actually happens when people talk.

The pattern was predictable. Meeting ends. Decisions get reconstructed from memory.

Action items turn into “I thought you were handling that.” Multilingual conversations lose nuance somewhere between the spoken word and the follow-up email.

Our agent works inside the conversation, not after it. Every word gets captured accurately. Every participant stays included, regardless of language. Every follow-up has clear documentation.

This isn’t about recording calls. It’s about building organisational memory that people actually trust.

![VideoTranslatorAI’s real-time interpreter](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/722aaaea7b6fd89c483167339c915daba98848cb5ed5bfa4386d81201d88718f.gif "VideoTranslatorAI’s real-time interpreter")

[VideoTranslatorAI’s](https://agent.videotranslator.ai) real-time interpreter

## **The Test Worth Taking**

Voice AI is a stress test for organisational readiness.

Can your compliance processes survive automatic documentation? Can your teams operate with full transparency into what was actually said? Can your workflows handle the accountability that comes with real-time capture?

If yes, voice AI multiplies your capabilities.

If no, you’ve just diagnosed exactly what needs to change.

Either outcome is valuable. But only one of them stays comfortable.

The 95% of failed pilots didn’t fail because AI isn’t ready.

They failed because organisations weren’t.

The question is whether yours is different.

## Read more

*   "[Yes, Okay, Thank You" and Your Child Just Failed Another Grade](https://videotranslator.ai/blog/yes-okay-thank-you-and-your-child-just-failed-another-grade/)
    
*   [The Teletubbies' Vacuum is Better Engineering Than Tesla's Optimus](https://videotranslator.ai/blog/teletubbies-vacuum-is-better-engineering-than-tesla-optimus/)
    
*   [Build in Public Hurts Startups. Do It Anyway.](https://videotranslator.ai/blog/build-in-public-hurts-startups-do-it-anyway/)

95% of AI Pilots Fail. Voice AI Explains Why.

Why real-time AI exposes broken workflows A company just launched an AI pilot. The demo was impressive. The executives are excited. The budget is approved.

A teacher finishes explaining a child's reading struggles. The parent across the table nods politely, smiles, and says "Yes, okay." 

But something is wrong. The teacher can see it in the parent's eyes: confusion, uncertainty, the desperate attempt to follow along.

The meeting ends. Nothing changes. The child continues to fall behind.

This is the daily reality for millions of immigrant families worldwide. Parents who are deeply invested in their children's education but find themselves locked out of the conversations that matter most. 

They miss crucial information about academic struggles. They can't advocate for special services their children need. They sit in parent-teacher meetings nodding politely, understanding nothing.

If this is your experience, **_you're not alone. And it's not your fault._**

## **Invisible Wall for Immigrant Parents**

![Photo by Jhon David on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/3b36d748883c221309623b06d92250abb98a790c6d515e415787412e851e86df.jpeg "Photo by Jhon David on Unsplash")

Yesterday, I was doing some research for my upcoming app demo for my education sector client. Until I found a statistic that stopped me cold. 

**_“During 2023-2024 school year, around_** [**_79%_**](https://www.languageline.com/blog/study-school-involvement-drops-dramatically-for-non-english-speaking-parents) **_of Spanish-speaking parents who try to participate in school activities report that language barriers make it difficult.”_**

Let that sink in. Nearly four out of five parents who _want_ to be involved in their child's education are hitting a wall before they even start.

Even with the advancement of technology, the gap is widening, not shrinking. In 1999, 69% of non-English-speaking parents attended general school meetings. By 2024, that number had dropped to just 55%. Meanwhile, English-speaking parent attendance rose to 87%.

That's a 32-percentage-point gap. And behind every point is a child whose parent couldn't advocate for them.

## **A Real Story from Philadelphia**

Right after stumbling upon that heartbreaking statistic, I came across an article from 2025. It was a story from [Zulma Guzmán](https://www.chalkbeat.org/philadelphia/2025/07/28/latino-families-struggle-with-translation-communication-in-school-system/), an immigrant from El Salvador who now calls Philadelphia home. 

Zulma’s experience perfectly echoes the statistic: the frustration felt by Spanish-speaking parents as they navigate a school system that often feels designed to exclude them.

![Source: Chalkbeat](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/45b6f5753746d6e0ab34411f8c72b30ed778cbbf85eac89b747294002460d104.jpeg "Source: Chalkbeat")

Parents miss information about the academic interventions their children need. They don't understand the health services available through schools. They can't decode report cards or respond to urgent communications about their child's behaviour.

On the other hand, Philadelphia's Latino student population has increased by [**51%**](https://www.chalkbeat.org/philadelphia/2025/07/28/latino-families-struggle-with-translation-communication-in-school-system/) **since 2018**. That's remarkable growth. But the translation services haven't kept pace. And the children caught in the middle are paying the price.

_"We need more support,"_ Zulma says. 

Coming from someone who has witnessed this crisis up close, it's not a complaint. 

It's a plea.

## **Teachers Feel It Too**

![Photo by Vitaly Gariev on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/ec80c80600331ba2eae79d36ad9b1e88204062a24704f5d7b30ec4b62211d18f.jpeg "Photo by Vitaly Gariev on Unsplash")

This problem isn't just on one side of the desk.

**56% of teachers** in the US report being concerned about language barriers when communicating with ESL parents. 

They can see students struggling. They know what interventions would help. But they can't have the necessary conversations with the families who need to hear it most.

Consider what that means in practice. A teacher notices a student falling behind in reading. The standard approach would be to meet with parents, explain the situation, and develop a plan together. But if the parents speak limited English, that conversation becomes nearly impossible.

Some schools have bilingual staff. Some hire professional interpreters for important meetings. But these resources are expensive, inconsistent, and often unavailable when teachers need them most.

## **The Change We Can Make**

I've spent years building real-time translation technology. The applications I imagined were corporate: international business meetings, global team collaboration, conference interpretation.

Then I started hearing stories like Zulma's. I started reading the research about what happens when parents can't engage with schools. The outcomes are devastating.

In Australia, where I live now, more than 300 languages are spoken, and over one in five people speak a language other than English at home. Schools here face many of the same challenges as Latino parents in Philadelphia.

That’s when something clicked.

The technology I’d been building didn’t have to stop at translating corporate meetings. It could be used where understanding actually changes lives.

It could help a teacher explain to a parent why their child needs extra support in maths. It could help a parent ask questions about their child's progress. It could turn a polite, confused nod into a real conversation.

And I’m delighted to say I wasn’t wrong.

Recently, [VideoTranslatorAI](https://agent.videotranslator.ai) worked with an Australian school facing a delicate situation: a Korean Year 9 student wanted to attend an overnight camping trip for a friend’s birthday.

Using our real-time multilingual video call, the school was able to have a full, unhurried conversation with the parents. Every risk was explained. Every question was answered. Consent wasn’t just given. It was truly informed.

![VideoTranslatorAI multilingual video call platform featuring AI-generated faces. Real clients' faces are not displayed to protect their privacy.](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/7160abfd182046311402821ce5f5524748bd942dfb46779bb5b419be167165a2.png "VideoTranslatorAI multilingual video call platform featuring AI-generated faces. Real clients' faces are not displayed to protect their privacy.")

The student attended the trip. The school stayed legally protected. The parents felt respected and included in the decision.

Read the full story about this: [**What Happens When Consent Gets Lost in Translation**](https://videotranslator.ai/blog/what-happens-when-consent-gets-lost-in-translation)

## **An Invitation**

The tools exist. The research supports their impact. The need is urgent and growing.

This isn't about replacing human connection with technology. It's about enabling human connection that language barriers currently prevent. 

An interpreter tool doesn't substitute for a caring teacher or an engaged parent. It lets them actually work together.

Zulma and the parents she advocates for aren't asking to be coddled. They're asking to participate in a system that affects their children's futures. They're asking for the same access that English-speaking parents take for granted.

We can close the 87-55 gap. Not by changing parents, but by changing the systems that exclude them.

If you're an educator, administrator, or parent navigating these challenges, I'd genuinely like to hear from you. What's working? What's failing? What would actually help?

This problem is too important to solve in isolation. It requires teachers, parents, and technologists working together.

This is a conversation worth having. And thanks to technology, it can finally happen in any language.

## Read more

*   [The Teletubbies' Vacuum is Better Engineering Than Tesla's Optimus](https://videotranslator.ai/blog/teletubbies-vacuum-is-better-engineering-than-tesla-optimus)
    
*   [Build in Public Hurts Startups. Do It Anyway.](https://videotranslator.ai/blog/build-in-public-hurts-startups-do-it-anyway)
    
*   [How to Get 30 Minutes Back After Every Meeting](https://videotranslator.ai/blog/how-to-get-30-minutes-back-after-every-meeting/)

"Yes, Okay, Thank You" and Your Child Just Failed Another Grade

Inside the silent crisis affecting millions of immigrant families A teacher finishes explaining a child's reading struggles.

Something about Tesla Optimus actually makes my skin crawl.

It's not rational. The robot is impressive. Smooth movements, precise grip, balanced gait.

But every time I watch it in my feed, I get that familiar shudder. The _uncanny valley_ pulling me back.

First month of 2026 has randomly turned my Twitter into a humanoid robot showcase.

![XPENG's next gen humanoid robot, IRON](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/8527ad5bd1bb53065dcca74011b2ff31e9acfd3de923795de0ab970b2139ec61.png "XPENG's next gen humanoid robot, IRON  (Source: X, @TheHumanoidHub)")

[Figure 02](https://www.figure.ai/news/production-at-bmw) at BMW plants. [1X Neo](https://www.1x.tech/neo) entering homes. Chinese firms deploying hundreds of sanitation units. The message: **_this is the future._**

But I've spent weeks now trying to articulate why this trend leaves me cold. And I think I've finally worked it out.

We're building the wrong kind of "humanoid." 

## **Reasons Why Humanoid Design Makes No Sense**

![Photo by julien Tromeur on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/5984fe20d54b9c7c01de56d1c42499371bdb2aad11c5bdd5d5e993c988ae599a.jpg "Photo by julien Tromeur on Unsplash")

### 1\. Human Body isn't a Good Blueprint

I'm going to say something that sounds obvious once you hear it: the human body is fragile and wildly inefficient for most tasks.

Our spines ache. Our knees blow out. We get tired after a few hours of repetitive work. Evolution optimised us for survival on the African savanna, not for warehouse logistics or precision manufacturing.

_So why are we giving robots these same limitations?_

A robot with four arms could complete assembly tasks twice as fast as any humanoid. 

A wheeled base beats bipedal walking for stability and energy efficiency. 

We used to understand this intuitively. Remember those cartoons where robots had extra limbs and could transform? That was better engineering thinking than what billion-dollar companies are pursuing today.

Teletubbies’ noo-noo is better at vacuuming than a humanoid holding a vacuum. Full stop.

![The famous vacuum robot from the Teletubbies show](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/f0e74bfd5ef5d3040b4a1aed0351d0a65c569ad3bc7152898a0abff7d8020c03.gif "The famous vacuum robot from the Teletubbies show")

### 2\. Energy Inefficiency

Walking on two legs is one of the least efficient forms of locomotion. Wheels, tracks, and even quadrupedal movement are all more energy-efficient than bipedalism. 

Yet we're building robots that burn battery power maintaining balance whilst walking, a problem that wheeled robots solved decades ago.

Current humanoid robots run for approximately four hours on a single charge, largely because bipedal movement is so energy-intensive. We're engineering inefficiency directly into our most advanced machines.

### 3\. Structural Fragility

The humanoid form prioritises mobility over durability. Joints that move in multiple directions are inherently more fragile than fixed connections. Bipedal balance requires constant micro-adjustments that increase wear on components.

We're building robots that will need the same joint replacements and structural repairs that plague human bodies, when we could build sturdy, stable platforms that last decades without major maintenance.

@[youtube](https://www.youtube.com/watch?v=kMfcGfRO0R8)

### 4\. Uncanny Valley Problem

There's a psychological phenomenon where humanlike robots that aren't quite human enough trigger discomfort and revulsion. 

The more closely robots resemble humans without being indistinguishable, the more unsettling they become.

We're spending billions developing robots that make people uncomfortable when we could build obviously mechanical systems that people find aesthetically neutral or even appealing. 

Industrial robots don't trigger uncanny valley responses because they don't pretend to be human.

### 5\. Overengineering for Most Tasks

![Photo by Accuray on Unsplash](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/c3a9a381d15a9116863a5d90a0e26d8c009210a0ec153f48feb6181549d9a6f9.jpg "Photo by Accuray on Unsplash")

Most tasks robots need to perform don't require human form. Warehouse logistics, manufacturing, agriculture, cleaning, inspection, and maintenance are all better served by specialised designs.

The humanoid form is a jack-of-all-trades approach that makes every task slightly worse than it could be with purpose-built robotics. 

We're choosing versatility over excellence, and in the process, creating machines that excel at nothing.

## **The Humanoid Technology We Actually Need**

The real breakthrough won't be robots that look like us. It will be systems that understand us. 

At [VideoTranslatorAI](https://videotranslator.ai/), we’re building tool for cross-lingual communication, systems that can translate, transcribe, and summarise meetings in real time whilst preserving the subtle meanings that get lost in translation. 

That's the kind of "humanoid" capability that matters: understanding human communication well enough to bridge language and cultural gaps.

When I watch humanoid robots struggle to walk on a runway or fold laundry in ways that would be trivial for purpose-built machines, I see billions of dollars chasing the wrong goal.

We don't need robots that mimic our physical form. We need intelligent systems that complement our capabilities, handle tasks we're poor at, and integrate seamlessly into our workflows regardless of their physical appearance.

A meeting assistant that captures every point in a multilingual discussion is infinitely more valuable than a robot that can physically attend the meeting but understand nothing being said.

![VideoTranslatorAI’s real-time interpreter](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d11428991e3c40270a93f7e793dfb6f3568b95946835d4fb891387aac9144555.gif "VideoTranslatorAI’s real-time interpreter")

## **The Path Forward**

I won't pretend humanoid robots have zero applications. If you need a robot to navigate a space literally built for human bodies, the form factor makes sense.

But the breakthrough that actually transforms how we work and communicate won't come from perfecting synthetic knees. It will come from AI that genuinely grasps human language, context, and emotional nuance.

That's the humanoid revolution worth waiting for.

Walking robots make good demos. The understanding ones will change everything.

## Read more

*   [Build in Public Hurts Startups. Do It Anyway.](https://videotranslator.ai/blog/build-in-public-hurts-startups-do-it-anyway/)
    
*   [How to Get 30 Minutes Back After Every Meeting](https://videotranslator.ai/blog/how-to-get-30-minutes-back-after-every-meeting/)
    
*   [What Happens When Consent Gets Lost in Translation](https://videotranslator.ai/blog/what-happens-when-consent-gets-lost-in-translation/)

The Teletubbies' Vacuum is Better Engineering Than Tesla's Optimus

The humanoid hype is engineering inefficiency into our most advanced machines Something about Tesla Optimus actually makes my skin crawl. It's not rational.

Nikita Bier knows how to build viral apps. Two acquisitions totalling over $30 million, advisor to Solana, and now Head of Product at X.

His track record speaks for itself. So when someone with his credentials warns founders about building in public, it's worth listening to.

In his recent X post (January 27th 2026), he argued that Build in Public is "a wrong move for startups" sparked genuine debate.

![Screenshot 2026-01-28 at 22.09.16.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/5a34cfd66c9d83515c7deb5f86ced0ec23cd36147a23811a125744cfaba4fe2e.webp)

Source: [X](https://x.com/nikitabier/status/2016186182628249791?s=20)

His case is worth taking seriously. But I think he's right about some things and wrong about others, and the distinction matters.

## **What Nikita Gets Right**

![Nikita Bier](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/9fd809427142d77df912b9b13da5569a54d110375a96c79d6eb3ab12f73ff007.webp)

Let's start with the valid concerns, because dismissing them entirely would be intellectually lazy.

Nikita argues that startups need room to make drastic changes. He's correct.

The path to product-market fit is rarely linear. You might start with a consumer app and end up building enterprise software. You might scrap entire features, rename the product, or pivot to a completely different market. That flexibility is essential.

He's also right that building in public can create psychological pressure. When you've publicly committed to a direction, pivoting feels harder. There's ego involved, and the friction of admitting you were wrong in front of an audience.

And yes, some founders do get distracted by the performance of building in public. They optimise for engagement instead of product quality. They spend more time crafting threads than talking to customers.

As someone who builds in public, I admit that these are real risks. Nikita isn't manufacturing problems out of nothing.

## **Where The Argument Breaks Down**

![Photo by BoliviaInteligente on Unsplash.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/3584c92467b669efab5fb393c3c5251c0be35ef99d5312ad7a4181e6fdeed486.webp)

Here's where I diverge from his conclusion.

Nikita's framework assumes startups have "**finite attention**" they need to protect.

This makes sense for established companies with existing audiences and brand equity to preserve. But for early-stage startups?

The average pre-seed founder isn't managing audience expectations. They're trying to get anyone to notice they exist. Their problem isn't attention _overload_. It's attention _scarcity_.

For every startup that confuses their audience with pivots, hundreds die in complete silence. They build something nobody hears about, launch to indifference, and quietly shut down.

The risk of obscurity dwarfs the risk of "narrative complexity."

## **The Attention Economy Works Differently for Unknowns**

![Photo by Melanie Deziel on Unsplash.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/ecb198499a6e51897f7c6cfd54279d1d085cf121a3e91e48b610b71868ccfddf.webp)

Nikita's advice implicitly assumes a model where attention is a battery: you start with a charge, and every public update depletes it. Better to conserve that energy for one big precision launch.

But for startups starting from zero, attention isn't a battery. It's a muscle. You build it through consistent exercise, not conservation.

Building in public isn't spending attention. It's generating it.

Every update, every behind-the-scenes moment, every transparent reflection is an opportunity to be discovered. Someone finds your post, checks your profile, gets intrigued, follows along. Three months later, they become an investor, a customer, or an advocate.

The compounding effect of consistent visibility is hard to replicate through any other means.

## **Strong Founders Already Know When to Ignore The Room**

![Photo by Phil Cln on Unsplash.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/2da3030691c49784574e2e29165decbe68ef402a48d0bd5e7bf302599f132ca6.webp)

Nikita worries that having an audience makes it _"difficult to make those changes that only you can understand."_

![Screenshot 2026-01-28 at 22.15.08.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/b08e0b53b7bc918c0dd6cd5e70621933a1ea381eed13c2ae4f34cfdedd8fb139.webp)

I'd push back gently here. Founders face pressure from all directions: investors, advisors, co-founders, early customers.

The pressure of internet followers is actually the mildest form of scrutiny you'll encounter.

Founders who can't make bold moves because strangers might be confused are going to struggle with far more consequential pressures down the line. The ability to gather input, then ignore it when necessary, is a core founder skill.

Building in public doesn't create that challenge. It just surfaces it earlier.

The best founders I know treat their audience as a sounding board, not a governing board. They share openly, collect signals, then make their own calls anyway.

## **Accountability isn't Always A Bug**

![Photo by Timon Studler on Unsplash.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d473b24a650a875f424910be2c3594b68c4dbb137ed34474d6ccd16d3e0cb3df.webp)

Yes, pivoting is harder when people are watching. But consider whether that friction is always negative.

Building in public forces you to articulate your reasoning. When you pivot, you have to explain why, which often clarifies your own thinking.

It creates a natural check against the kind of aimless thrashing that kills startups: changing direction every few weeks because nothing was ever committed to publicly.

The discipline of explaining your decisions to an audience, even a small one, reveals whether a pivot is strategic or just panic. Sometimes the best thing an audience does is make you pause and ask:

_can I actually defend this?_

If you can't, maybe the decision needs more thought.

## **Early Adopters Beat Launch Day Crowds**

Nikita suggests building quietly, then launching with "precision" after achieving PMF.

But consider who shows up for a cold launch versus a warm one.

Strangers who discover your polished product have no context. They'll churn at the first friction point. They have no reason to give you the benefit of the doubt.

![Fail In Public.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/22972802f0d511c788e8f85a3d37a9616f6bebebb5494a4ac8e981c4e5d07d25.webp)

The people who followed your journey?

They watched you struggle with that one feature for months. They understand your vision because you've explained it repeatedly.

When they encounter bugs, they report them instead of leaving. They tell their friends because they feel invested in your success.

I'll take 500 engaged followers over 5,000 cold visitors every time.

## **The Internet Forgets Faster than You Think**

![Photo by ROBIN WORRALL on Unsplash.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/fa35748677376050a6a6b9ab780f632913cd334700c8bccb76a1846fc0d1f6a8.webp)

Nikita warns that narratives become "so complex that no audience can keep up."

In my experience, this concern is overblown. The internet has a short memory. Most people who discover you won't scroll back through months of posts. They'll see your current positioning and current product. Your past pivots are invisible to them.

And for the few who do follow your entire journey? They often become your biggest advocates. They love origin stories. They appreciate transparency.

Think about the startup legends we celebrate: [Airbnb](https://www.cnbc.com/2023/04/18/airbnb-ceo-says-he-wooed-first-investors-with-boxes-of-cereal.html) selling cereal boxes, [Slack](https://www.justanotherpm.com/blog/what-can-product-managers-learn-from-the-growth-of-slack#:~:text=The%20Origin%20of%20Slack,missing%20this%20age%2Dold%20game\)) starting as a video game, [Instagram](https://www.instagram.com/reels/DQeUMUPkz3g/) beginning as a cluttered check-in app.

The pivots aren't embarrassing footnotes. They're part of the mythology.

## **It's about Distribution, Not Just Feedback**

Nikita frames building in public primarily as a feedback mechanism. Established companies do that better, with larger user bases and proper research teams.

But for startups, feedback isn't the main value. Distribution is.

Every update is content. Content gets discovered, shared, and amplified. Building in public is marketing that costs nothing but time. It's how you attract co-founders, early employees, investors, and future customers without a budget.

Startups can't afford PR agencies or paid acquisition at scale. What they can afford is transparency. Building in public converts your daily work into a distribution engine.

## **The Real Trap (And It's Not What Nikita Describes)**

![Photo by Kelly Sikkema on Unsplash.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/a4fca202ef213cfb93b68a3b75c822401f1eb565e906990cb355e735704e75b6.webp)

If I'm being honest about the risks, the genuine danger isn't narrative confusion.

It's that building in public can become procrastination dressed up as strategy. Some founders spend more energy on update threads than on actual product work.

The dopamine hit of engagement replaces the harder work of customer conversations and shipping features.

That's a real trap. But it's a discipline problem, not an inherent flaw in the approach. The solution is self-awareness, not silence.

## **The Asymmetric Bet**

Here's what it comes down to.

Building in public costs nothing material. No budget. No permission. Just your time and willingness to be transparent.

If it doesn't work? You can always go quiet. You can rebrand. You can start fresh. The internet will forget, and you'll have lost nothing but hours.

If it does work? You've built something valuable: an audience, a distribution channel, a community of people invested in your success. That's hard to replicate through any other means.

## **Who This Advice is Actually For**

![Screenshot 2026-01-28 at 22.23.57.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/ffbec8580e3198b4a000c3e760866640366f0d25846bfd8775ef5d66592ef1b9.webp)

Nikita's caution makes sense for founders at established companies launching new products. When you already have an audience with expectations, managing narrative becomes important.

But for the founder starting from nothing? The calculus is different.

Building in public isn't mandatory. If you're working on deep tech with IP concerns, stay quiet. If competitors will clone you instantly, be strategic about what you share. If you genuinely cannot filter external opinions without losing focus, protect your attention.

But for most consumer and SMB startups, the theoretical risk of narrative confusion is vastly outweighed by the practical benefits: distribution, accountability, audience building, and early adopters who actually care.

Most startups don't die from too many eyes on them. They die from too few.

## **The Bottom Line**

![Build in Public Hurts Startups. Do It Anyway - Blog.webp](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d44e37a19ffcd69615b3df394e9a6eb4a05fed2f851c0f403551c90438db4eec.webp)

Nikita Bier is sharp, and his concerns come from real experience. But that experience was earned at companies with existing audiences. The advice doesn't transfer cleanly to founders starting from zero.

Building in public isn't right for everyone. But for many founders, it's one of the few genuine advantages available. It costs nothing. The downside is limited. The upside is substantial.

If you're building something and nobody knows about it, the risk isn't that you'll confuse people with your pivots. The risk is that nobody will be around to notice when you finally get it right.

That's the risk worth worrying about.

_Where do you land on this debate? I'm genuinely curious to hear from founders who've tried both approaches._

## Read more

*   [How to Get 30 Minutes Back After Every Meeting](https://videotranslator.ai/blog/how-to-get-30-minutes-back-after-every-meeting/)
    
*   [What Happens When Consent Gets Lost in Translation](https://videotranslator.ai/blog/what-happens-when-consent-gets-lost-in-translation/)
    
*   [Sales Didn’t Kill My Deals. Silence Did.](https://videotranslator.ai/blog/sales-didnt-kill-my-deals-silence-did/)

Unpacking Nikita Bier's argument and where founders misapply it Nikita Bier knows how to build viral apps. Two acquisitions totalling over $30 million,...

Build in Public Hurts Startups. Do It Anyway.

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