Today is International Labour Day.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

\---

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

Labour Day in the Age of AI Layoffs - Tat Banerjeee

Labour Day in the Age of AI Layoffs

If you’re on X, you’ve probably seen it by now. An AI Optimist Linus Ekenstam ([@LinusEkenstam](https://x.com/LinusEkenstam)) posted a photo of his palm to ChatGPT and got a full palm reading back.

Within hours, hundreds of people were doing the same thing. It’s fun, it’s shareable, and honestly, it’s a little addictive.

![Source: X](/api/media/elephantstripes-prod/cms/originals/blog-inline/832db00522b5a97674b504fbbb7f030b4e0dad0dd51fd8bd6448486eb7a65be3.avif "Source: X")

Then another account posted a meme that stopped a lot of people mid-scroll.

**_“Sir, they’re uploading their 4K biometric data to ChatGPT for free. Surveillance agencies spent years and billions trying to get this.” —_** [**_@_**shiri\_shh](https://x.com/shiri_shh/status/2048625984077971919/photo/1) via X

![](https://miro.medium.com/v2/resize:fit:1400/0*t4gvRO0DcWvxLVrP)

The meme uploaded by @shiri\_shh, along with his satirical tweet.

The community note on Ekenstam’s original post was even more direct:

> _“Uploading palm photos shares extractable fingerprints and other biometric data that cannot be changed if compromised or misused.”_

This is worth taking seriously. Not to kill the fun, but because this is exactly the kind of moment where a charming viral trend quietly creates a data problem people will not notice for years.

## **The Thing About Biometric Data**

![Photo by Onur Binay on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*KyBf80nw-Nu3VNZ1 "Photo by Onur Binay on Unsplash")

Most people have a rough intuition about data privacy. They know not to share their password. They are cautious about their bank details. They have accepted, reluctantly, that their email address and phone number are already widely available.

Palm prints are different in kind, not just degree. Biometric data, which includes facial geometry, fingerprints, iris scans, voice patterns, and palm prints, is permanent.

If your password leaks, you change it. If your credit card is compromised, you get a new one. But if your palm print, your fingerprint, or your detailed facial geometry ends up in a database that gets breached? There is **no replacement.** That hand is the hand you have for the rest of your life.

[The US Department of Justice](https://www.justice.gov/archives/opa/media/1382526/dl) made this precise point in a December 2024 final rule that explicitly restricted transactions involving bulk biometric data, including facial images, palm prints, and fingerprints. The rule cited “extraordinary” national security concerns and flagged the risk that this data could be used to develop and enhance AI capabilities, among other misuse scenarios.

When millions of people photograph their palms for a fun chatbot reading, each image becomes a high-resolution biometric sample sitting on a server somewhere. But they’re doing that without understanding how long it’s retained, who can access it, or what happens in the event of a breach.

The answer to all three questions, in most cases, is: **you genuinely don’t know.**

And that uncertainty is the problem.

## **The Free AI Photo Editor Problem I Raised Last Year**

![The viral AI winter photoshoot in 2025](/api/media/elephantstripes-prod/cms/originals/blog-inline/494e30fa73029ea128832203b461085c13da9230eb270fc1f853b266f9b8e84f.avif "The viral AI winter photoshoot in 2025")

I wrote about this dynamic last year in the context of [free AI photo editors](https://elephantstripes.ai/blog/your-face-in-ai-training-data-you-cant-get-it-back). A fun tool goes viral. Caricature style. Ghibli filter. Winter photoshoot for the Christmas season.

The friction is zero, the output is immediately shareable, and the upload feels trivial. Millions of people participate because _why not?_

However, it’s worth noting that what they are uploading is detailed biometric or facial data to a _s_erver they know nothing about, with **terms of service** that are rarely read. The data retention policy is not something people click through to understand. And the downstream use, whether for model improvement, sale to data brokers, or exposure in a future breach, is genuinely uncertain.

Next time you generate a crowd scene with an AI image tool, take a moment and look at the faces in the background. Some of those faces are procedurally generated from statistical distributions, but some of them feel oddly specific. They appear too detailed for random generation and too distinct to be purely synthetic.

This raises an important question:

> **_Where do you think those faces come from?_**

I am not making a definitive claim about any particular platform’s practices. What I am saying is that the question is worth asking.

What’s happening now with palm readings is structurally identical. Even if a major platform like OpenAI handles the data responsibly, the broader ecosystem around it does not stay contained. Data brokers exist and operate at scale. Corporate acquisitions happen. APIs get licensed to third parties. Policies that protect you today can change tomorrow.

The entertainment value is immediate. The risk is diffuse and slow. That is exactly the dynamic that makes it easy to dismiss until it is not.

## **The Actual Risk Breakdown**

![Photo by kartik programmer on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*PT4GHKMLXI7JL74Q "Photo by kartik programmer on Unsplash")

Let me be specific about what the concerns actually are, rather than vague about the general direction.

**Irreversibility.** A compromised palm print cannot be changed. Stolen biometric templates enable spoofing, fraud, and AI-assisted impersonation in ways that persist for the lifetime of the individual. This is not theoretical. It has already happened in documented breaches.

**Data handling uncertainty.** Uploaded content to a consumer AI platform may be stored, reviewed for safety, or used to improve model performance. Policies emphasise user controls and opt-out mechanisms, but the enforcement of those mechanisms in edge cases such as breaches, subpoenas, company acquisitions, or policy updates is not something users can verify independently.

**Cultural normalisation.** Each viral biometric sharing trend lowers the collective threshold for what feels acceptable. When palm prints become as casually shared as selfies, the protective instinct that would otherwise pause people before uploading degrades. That cultural shift is slow, hard to reverse, and genuinely costly.

**Ecosystem effects.** High-quality palm and facial data, even if the original platform handles it responsibly, contributes to the broader capability set for biometric modelling. That capability benefits legitimate applications and potential misuse with equal efficiency.

## **Practical Steps That Actually Help**

![Photo by Towfiqu barbhuiya on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*5PQ7H4Gjrh0sezEX "Photo by Towfiqu barbhuiya on Unsplash")

You don’t need to swear off AI to manage this well. Here’s a reasonable approach:

**Opt out of training data use.** Most major platforms have a setting for this. Find it and use it. It takes less time than the palm reading itself.

**Reduce image quality for casual experiments.** A blurrier or partially obscured image still allows you to play with the feature without handing over a precise biometric sample.

**Use enterprise or paid accounts** where the terms around data use are more explicit, and your leverage as a paying customer is higher.

**Explore local AI tools** for anything genuinely sensitive. Local models process data on your own device and don’t transmit anything externally.

**Apply a simple test before uploading.** Would you post this image permanently and publicly in full detail? If not, that hesitation is telling you something worth listening to.

And please, regardless of what platforms you use for entertainment, do not rely solely on palm or fingerprint biometrics for your phone unlock or banking authentication if those features can be spoofed by someone who has access to a detailed photograph of your hand.

## **The Bottom Line**

Linus Ekenstam’s palm reading post is delightful. The engagement is understandable. ChatGPT producing a seemingly accurate personality reading from a hand photo is genuinely impressive and a little uncanny, which is exactly why it spreads.

But the fun and the risk are not mutually exclusive. Hundreds or even thousands of people uploading high-resolution palm photographs because a viral post told them it was cool is exactly the kind of moment that produces large biometric datasets without any meaningful informed consent process.

You are allowed to try the thing. Just do it with the same care you would apply to sharing a sensitive document. Ask yourself whether the entertainment value is worth a permanent, irrevocable data point. Most of the time, a quick crop and a lower resolution will let you enjoy the experience with a fraction of the exposure.

The technology is not the problem. The assumption that casual uploads are consequence-free is.

* * *

_At Elephant Stripes, we prioritise security at every stage of development, ensuring your data is protected with the latest encryption technologies and best practice._

_Ready to reinvent your product or workflow for the AI era? Reach out to_ [_ElephantStripes_](https://elephantstripes.ai/) _custom AI-app development studio._

ChatGPT Image 2 Palm Reading is Impressive. Don’t Do It.

ChatGPT Image 2 Palm Reading is Impressive. Don’t Do It - Tat Banerjee

Something interesting is happening on X right now.

A handful of viral posts are declaring that soaring AI costs are reversing the great layoff wave, that token bills have grown so large that it is suddenly cheaper to just hire a human developer again, and that the pendulum is swinging back.

![](/api/media/elephantstripes-prod/cms/originals/blog-inline/f5bda37fe32e54c0385a5d279795ac4f8199510b2dde6ad89dd296f1ec26e93b.avif "Source: X")

The posts making this case have been widely shared. The logic is emotionally satisfying. And it is mostly **_wrong_**_._

Here is the thesis:

> _“The AI layoffs gold rush is hitting a wall, with token bills that rival the salaries just cut.”_

That framing is attention-grabbing. But it conflates a short-term pricing friction with a structural reversal that simply is not happening. AI costs are a speed bump, not a U-turn.

Let me explain what is actually going on, and why the smarter move is neither to panic-hire humans back nor to blindly automate everything and hope for the best.

## **The Data on Token Prices Tells a Different Story**

![Source: X](/api/media/elephantstripes-prod/cms/originals/blog-inline/c4e138f949b05935a8a5e357535a8816df09b52a845c49d6b9c10abe57f7959e.avif "Source: X")

The viral posts are reacting to real sticker shock.

Developers using Claude Code as a daily coding agent report API costs running anywhere from $500 to $2,000 per month on the API. One documented case showed eight months of intensive daily usage consuming 10 billion tokens, which at standard API rates would have cost over $15,000.

Anthropic’s recent decision to test removing Claude Code from its $20-per-month Pro plan, confirmed in April 2026, is a clear signal that flat-rate agentic AI is straining the economics on both sides of the table.

But zoom out one level and a different picture emerges.

GPT-4 launched in March 2023 at roughly $37.50 per million tokens. By August 2025, the cost-efficiency frontier had reached $0.14 per million tokens. Sam Altman put it plainly: _“The cost to use a given level of AI falls about 10x every 12 months.”_ That is not a Moore’s Law pace. It is substantially faster.

Budget-tier models tell the same story. The GPT-5 nano currently sits at $0.05 per million input tokens. DeepSeek R1 launched at $0.55 per million, undercutting competitors by roughly 90%. Grok 4.1 runs at $0.20 per million input. Open-source competition from models like Meta’s Llama family is compressing the floor even further.

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

The paradox is that despite these falling per-token prices, enterprise AI spend has tripled from $11.5 billion in 2024 to $37 billion in 2025.  
**_Why?_** Because companies are consuming vastly more of it. Usage is scaling faster than prices are falling. That is not a cost problem. It is a discipline problem. Discipline problems respond to process redesign, not to rehiring.

Reasoning models like o3 consume approximately 83 times more compute per task than a standard GPT-4o response. The frontier price stays relatively stable while yesterday’s frontier becomes the new budget tier.

So yes, frontier agentic AI is expensive right now. But the trajectory is relentlessly downward, and the companies treating the current sticker shock as a permanent equilibrium are misreading the moment.

## **The CFO’s Real Dilemma: Optics vs. Long-Term Cost**

![Source: X](/api/media/elephantstripes-prod/cms/originals/blog-inline/748c05e77257959775397f3765d1ec9350a56f1cac19fa61b09ae28ab19861b1.avif "Source: X")

Here is the honest problem finance teams are facing in 2026.

A fixed engineer salary is predictable. A token bill that scales with usage is not, and that unpredictability creates real discomfort for quarterly planning and board reporting. _“We cut 20 engineers and now we’re spending $3M on tokens”_ is not a comfortable slide to present.

But the short-term optics and the long-term total cost of ownership are two different conversations.

Variable costs expose something fixed costs hide: usage patterns. When a token bill blows up, it reveals that workflows were not designed with **efficiency** in mind. That is uncomfortable, but it is also actionable. You can optimise a token spend. You cannot easily renegotiate a salary band.

The companies getting this **wrong** treat AI as a direct swap: remove human, insert API call, observe savings. The companies getting this right are doing the harder work of redesigning workflows so that AI handles volume generation and humans handle validation, judgement and direction.

That redesign requires upfront investment. It requires a new kind of technical leadership, one that understands when to route a task to a $0.25/M input token model versus a $3/M input token model. And it requires measuring productivity by output, not by headcount.

The CFO who rehires to escape a token bill is solving the wrong problem.

## **The Hybrid Future: AI for Humans, Not Instead of Them**

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

The “AI vs. humans” frame is the wrong lens entirely.

The question was never “which is cheaper?” The question is always “which combination creates the most value?”

By Q2 2026, AI coding tools will contribute to over **60% of new code written on GitHub**. GitHub Copilot alone drives 46% of accepted code suggestions in enterprise environments ([The Board World, 2026](https://theb6oard.world/articles/technology/will-ai-replace-software-engineers-2026-reality-check)). Those engineers are not being replaced. They are being amplified.

The salary premium data confirms where value is migrating. AI engineers command a **12% salary premium** over traditional software engineers. LLM developers average $209K annually ([Hakia, 2026](https://www.hakia.com/news/software-developer-salaries-2026/)). The market is not moving away from human expertise. It is moving toward a specific kind: people who can orchestrate, evaluate and direct AI systems at scale.

Winners over the next 24 months will not be companies that replaced their workforce with AI. They will be companies that redesigned their workflows around agents and retained the humans who make those agents useful.

That looks like:

*   Smaller, more senior engineering teams with clear AI orchestration responsibilities
    
*   Product managers who can write, evaluate and iterate on prompts
    
*   Operations leads who understand token economics and routing logic
    
*   Leaders who measure what ships, not who attends stand-up
    

This is not a return to the old model. It is a compression of the old model into something leaner, faster and more interesting to work in.

## **Risks and Upsides: Being Honest About Both**

![Photo by Miguelangel Miquelena on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*li7ZdERb7WsMA7Kg "Photo by Miguelangel Miquelena on Unsplash")

The optimistic take can tip into cheerleading, so here is where I’d pump the brakes slightly.

**Talent atrophy is a real risk.**

If junior developers spend two years reviewing AI-generated code without writing any themselves, they do not develop the debugging instincts and architectural intuition that senior engineers carry.

The pipeline for 10x developers depends on a functioning pipeline for 1x developers first. Over-automate too fast and you hollow out institutional knowledge without realising it until a system breaks in a way no model has encountered before.

**Vendor concentration risk is underappreciated.**

The Anthropic pricing test affected 2% of users. Imagine it at scale, or imagine a frontier lab repricing significantly after you have rebuilt your entire product around their API. The cost stability that makes AI look cheap today depends partly on a competitive market that may not remain this competitive.

**Now for the upside, which is genuinely significant.**

One exceptional engineer with deep AI orchestration skills can ship what previously required a team of five. That is not an argument for keeping salaries down. It is an argument for paying the best people more and being far more deliberate about who you hire.

The velocity gains also compound in ways that individual productivity metrics do not capture. Shipping twice as fast for twelve months does not just double output. It generates learnings, customer feedback loops and product iterations that slower teams never reach. And those compounding loops create economic value that does not disappear when you replace headcount with agents. It distributes into lower prices, higher margins and entirely new product categories.

One 10x developer beats five 1x developers, not just on cost, but on speed, coherence and accumulated judgement. That has always been true. AI just makes the gap quantifiable.

## **What to Do With This (The Actual Call to Action)**

![Photo by S O C I A L . C U T on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*uVh4zKCkqRKiOxpa "Photo by S O C I A L . C U T on Unsplash")

**If you are a developer:** Upskilling in AI orchestration is no longer optional. The craft that commands $209K is not raw coding speed. It is the ability to build and manage agentic workflows, evaluate model outputs critically, debug AI-assisted systems and know when not to use AI at all. Start building things with agents now, even imperfectly. The reps matter more than the results at this stage.

**If you are a founder or leader:** Stop measuring headcount. Measure output per dollar, velocity per sprint and customer value delivered per quarter. Build accountability structures around results, not presence. The companies that will separate this year are the ones who do workflow redesign before their competitors do.

**For everyone watching the macro:** Productivity gains compound, and the economy expands when they do. This is the mechanism behind every prior technology transition, from electrification to the internet. This wave is faster and more general-purpose, but the outcome structure is the same. New categories of work emerge. They are just hard to name from inside the transition.

## **My Honest Bottom Line**

![](https://miro.medium.com/v2/resize:fit:1400/1*rKCHWWOn-wCWhZY83OZ9kg.jpeg)

Humans are not making a full comeback as the default cheaper option. Not now. Not in 2027. Probably not ever, for most roles.

AI costs are a speed bump, not a U-turn. The “full circle moment” going viral is real as a feeling. As economics, it does not hold. Token prices are falling structurally and fast. Total bills are rising because adoption is scaling, not because the underlying technology is getting pricier. Those are different problems with different solutions.

Dario Amodei’s fears about white-collar displacement overstate how quickly near-term displacement actually moves through organisations. The economics favour leverage over elimination. One exceptional person using great tools beats five average people using none. That has always been true. It is just now measurable in ways that make it undeniable.

The friction phase we are moving through right now, roughly 2026 through early 2027, is when smart companies separate signal from noise. The ones panicking about token bills and rehiring are reacting to noise. The ones quietly investing in AI-native workflows and building teams that know how to use them are reading the signal correctly.

The workforce story of this decade is not AI replacing humans. It is fewer, better humans creating more value with cheaper, smarter tools. That is not a consolation prize.

That is the actual opportunity.

* * *

_If you’re looking to rebuild your product or workflows for the AI era, contact_ [_ElephantStripes_](https://elephantstripes.ai/)_, our bespoke AI app development studio. We combine AI-driven speed with human judgment to build software that actually fits._

AI Costs are Rising. Will Human Workers Make A Comeback?

AI Costs are Rising. Will Human Workers Make A Comeback? - Tat Banerjee

AI can now write code, produce entire music albums, and design products overnight. The access is real. The speed is real.

And the **_slop_** is very, very real.

Scroll through any app marketplace, music playlist, or design portfolio today and you’ll find the same thing: technically competent output that feels hollow. Correct but somehow lifeless. Finished but never actually done.

Here’s what’s happening. The cost of execution has dropped to near zero. You can spin up a feature, a logo, a sample pack, or an entire codebase with a handful of prompts. But the thing that separates a product people love from a product nobody notices? That’s still the same as it’s always been.

> **Taste**.

This is the AI taste moat. And two creators explained it better than any researcher or analyst has: Peter Steinberger, the indie developer behind OpenClaw, on the builder’s side; and Diplo, one of the most commercially successful music producers of the past two decades, on the creator’s side.

Their message is the same even though their worlds are completely different. _Execution is now free. Taste is the last scarce resource._

Diplo put his entire career into a single sentence: _“I’m in a position now where I can use these tools really effectively because people already trust me for my taste and what I do.”_

Steinberger named the failure mode builders keep running into: agents that run all night and produce “the ultimate slop” because nobody with taste stayed in the loop.

Two industries. One thesis.

If you’re still optimising prompts instead of curating taste, you’re building on the wrong side of the moat.

## **Steinberger’s Warning: The Agentic Trap**

![Source: X](/api/media/elephantstripes-prod/cms/originals/blog-inline/ebb9f77d9ef2e76b2f96593cd2f7a1a611023d8a76fbcad597b116319b87f06f.avif "Source: X")

Peter Steinberger didn’t mince words.

_“You can create code and run all night and then you have like the ultimate slop because what those agents don’t really do yet is have taste.”_

That line landed hard across developer circles for a reason. It names something builders have been quietly noticing but haven’t wanted to say out loud. You can automate the output. You cannot automate the compass.

Steinberger went further:

_“They are spiky smart and they’re really good at things, but if you don’t navigate them well, if you don’t have a vision of what you’re going to build, it’s still going to be slop. If you don’t ask the right questions, it’s still going to be slop.”_

The agentic trap is what happens when you remove yourself from the process too early. You front-load a perfect spec, hand it to an agent, and walk away expecting something coherent to emerge. What you get instead is highly competent output with no internal compass.

Steinberger describes his own process differently. He starts rough. He plays with the output. He lets his next step emerge from what he sees and feels in the current state of the project.

_“My next prompt depends on what I see and feel and think about the current state of the project. But if you try to put everything into a spec up front, you miss this kind of human-machine loop. And then I don’t know how something good can come out without having feelings in the loop — almost like taste.”_

@[youtube](https://www.youtube.com/watch?v=AcwK1Uuwc0U)

This is the real insight. Not that agents are bad. Not that AI-assisted development is a mistake. It’s that the feedback loop between human taste and machine output is where the actual quality lives.

Think about it in concrete terms. A solo founder who iterates a feature 40 times in a single evening, each time responding viscerally to what feels off, ships something people actually use. The team that lets agents run unsupervised for 12 hours ships something polished and forgettable.

Agent scaffolding is improving fast. Self-critique loops are getting smarter. But the human taste anchor is still 100% required.

Prompting is the new typing. Taste is the new thinking.

## **Diplo’s Take on References, History, and the Pop Factory**

![](/api/media/elephantstripes-prod/cms/originals/blog-inline/7dfaae5a2bcae368911eda363399df9def69d584bac821c8cbf6882468d44452.avif "Source: X")

Diplo is not anti-AI. He made that very clear.

_“You’re not gonna win. There’s no fighting AI. You have to just work your best to be the best at it right now. You’re wasting your time.”_

He told a story about a young producer he had recently signed. They sat down together, fired up Udio, and made three goth-style beats in five minutes. The producer looked at him and said: _“I’m cooked, right?”_

Diplo’s answer was honest. _“I don’t know, man. Maybe.”_

But then he explained his own position. People already trust him for his taste. That trust is the product. The AI is just the fastest production line he has ever had access to.

His edge is not his software. It is not his prompt library. It is two decades of deep cultural fluency, from 1973 Jamaican dub to 2006 Baltimore club to 2008 blog-house and beyond.

It is the references. It is the history. It is the accumulated signal that lets him know, in five seconds, whether something is worth pursuing or not.

The tweet summarising his interview captured it cleanly: _“Taste and references matter A LOT. That is what makes me good at using AI because I know how to prompt something specifically. You have to have knowledge. You have to have history.”_

@[youtube](https://www.youtube.com/watch?v=zFVpJFFN3dI)

Most people pressing the same buttons do not have that layer. They work from descriptions. Diplo works from memory, intuition, and a reference library that took years to build.

And that gap only grows wider as the tools get better.

The 99% with access to the same AI tools still produce “good enough.” The 1% with genuine taste and deep references produce something that feels human-directed, alive, and worth paying for.

Your reference library is now more valuable than your prompt library.

## **Why References Beat Prompts: The New Moat Explained**

![Photo by Sanket Mishra on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*AVRhken-uV8Ov3VH "Photo by Sanket Mishra on Unsplash")

There is a simple way to understand why taste has become the moat.

Prompts are tactical instructions. They describe what you want. AI gets better at executing on tactical descriptions every month. By the time you have mastered a prompting technique, a newer model has made that technique mostly obsolete anyway. Prompting is a skill, but it is a depreciating one.

References and taste are something different. They are pattern recognition developed over years of deep attention to what works and why. They are emotional intelligence about what a piece of work needs to become. They are personal weirdness, accumulated preferences, and the willingness to trust your gut over the consensus.

Think of it this way. Prompts are the steering wheel. Taste is the map, the destination, and the reason you are driving in the first place.

The evidence is already visible. App stores are saturated with AI-generated productivity tools that are functionally identical and emotionally indistinguishable. Music streaming platforms are drowning in AI-assisted tracks that technically pass quality thresholds and generate no emotional response. The content that breaks through is increasingly the content that feels like a specific person made a specific decision about what it should be.

Compare the eras. In the 2010s, attention was the moat. Whoever captured and held attention could build a business around it. In the early 2020s, distribution was the moat. Whoever could reach an audience at scale had leverage. In 2026 and beyond, taste is the moat.

The counterpoint is real: models are getting better at simulating taste. They can mimic aesthetic preferences at a surface level. But simulation is not origination. The human who has actually spent years absorbing references, developing opinions, and learning to feel the difference between what is good and what is right still wins. Because they know what the simulation got wrong.

The premium product will always be the one that feels human-directed, even when 90% of the labour is AI.

## **A Practical Framework for Building Your Taste Moat**

![Diplo’s performance at Tomorrowland 2022 (Source: YouTube)](https://miro.medium.com/v2/resize:fit:1400/0*_UOqkP-_4hn0Z77p.jpg "Diplo’s performance at Tomorrowland 2022 (Source: YouTube)")

The good news: taste is a skill, not a gift. It grows with deliberate practice.

**Start with a taste vault**

Collect 50 to 100 specific examples of work you love across mediums. Not work that is considered good. Work that actually moves you, surprises you, makes you want to understand how it was made. Pull from this vault when you are prompting instead of describing from scratch. “Make something that feels like this specific thing” beats “make something good” every single time.

**Use the Steinberger loop, not the hands-off spec**

Do not hand agents a spec and disappear. Set 15 to 30 minute checkpoints. Stop. Interact with the live output. What feels wrong? What wants to become something? Let your next move come from that gut reaction, not from a document you wrote before you saw anything.

**Dedicate time to reference building**

One hour a week. Pick one artist, era, movement, or product category. Go deep, not broad. Write down what you notice. What surprises you. What you would steal. Share it publicly. Teaching what you find is one of the fastest ways to make it stick.

**The one honest question**

Before each prompt, ask yourself: do I actually know what this should feel like, or am I describing it from the outside? If you are describing, stop. Find a specific reference first.

## **So, Are You Cooked?**

Back to Diplo’s young producer.

The honest answer is: it depends entirely on what he does next.

If he treats AI as a shortcut to output, he will produce the same averaged content as everyone else using the same tools. If he spends the next two years deep in references, building his palate, understanding not just what sounds he likes but why they work and where they came from, he builds something the tools cannot replicate.

That is the actual play.

AI democratises execution. It makes taste more valuable. The creators and builders who understand this early will build moats that take years to cross.

Peter Steinberger is building software with this approach. Diplo is making music with it.

The tools are not the advantage. The taste behind them is.

* * *

_Share your own taste moat experiments in the comments or on X. The original interviews are worth your time:_ [](https://x.com/realBigBrainAI/status/2043668202061017177)[**_How OpenClaw’s Creator Uses AI to Run His Life in 40 Minutes | Peter Steinberger_**](https://www.youtube.com/watch?v=AcwK1Uuwc0U) _and_ [](https://x.com/jameygannon/status/2043788602467913966)[**_Why Diplo’s Weirdest Ideas Became the Biggest Songs in the World (Full Interview)_**](https://www.youtube.com/watch?v=zFVpJFFN3dI)**_._**

_Taste is not going away. It is finally getting the economic value it always deserved._

You Run Agents All Night. Then Get Slop.

On April 14, 2026, Gagan Biyani posted something on X that stopped many founders cold.

_“People have asked me how i feel about Udemy’s sale to Coursera. Honestly, I’m_ **_kinda pissed_** _about it”_

![Biyani's post on Coursera-Udemy merger (Source: X)](/api/media/elephantstripes-prod/cms/originals/blog-inline/2c0aa5a507bf0abb87f9266e59f4da51293f575771e913e56b12a189bcc2eaca.avif "Biyani's post on Coursera-Udemy merger (Source: X)")

By the time most people woke up the next morning, the post had 3,000-plus likes, hundreds of reposts, and hundreds of thousands of views. Not because the edtech industry loves drama. Because every founder who read it felt something twitch in their chest.

Biyani co-founded Udemy in 2010 with Eren Bali and Oktay Caglar. The platform built a genuine business: 75 million learners, 250,000 courses, enterprise contracts with more than half the Fortune 100, and an $800 million revenue run-rate by the time the acquisition was announced.

In December 2025, [Coursera](https://investors.udemy.com/news-releases/news-release-details/coursera-combine-udemy-empower-global-workforce-skills-ai-era) agreed to buy Udemy in an all-stock deal valuing the combined company at approximately $2.5 billion, with Udemy alone valued at roughly $930 million. Coursera shareholders end up holding around 59% of the combined entity. Closing is expected in the second half of 2026.

A story of edtech consolidation during a tough macro cycle. Fine. Except Biyani’s frustration is not about the macro environment. It is about a pattern that plays out in startup after startup, and almost nobody talks about it plainly until it is too late.

This is a masterclass in founder-investor dynamics, product stagnation, and the hidden costs of “professional management.” Biyani’s raw thread reveals five hard lessons every founder must internalise before their first big funding round, or risk watching their life’s work get sold instead of scaled.

## **From Outsiders to $800M: The Udemy Backstory**

![Udemy Co-founders (Source: Startuptalky.com)](https://miro.medium.com/v2/resize:fit:1260/0*Ul3ck9U4kA7P5kJe.jpg "Udemy Co-founders (Source: Startuptalky.com)")

The Udemy [origin story](https://pestel-analysis.com/blogs/brief-history/udemy) is the kind investors claim to love. Three outsiders with no pedigree and no savings, rejected 30 times by VCs before getting their first cheque, building a marketplace that let real practitioners, not just credentialled academics, teach practical skills to anyone who could afford a few dollars for a course.

It worked. After an early Series A and then a Series B led by Insight Venture Partners, the company grew fast. The post-Series B moment is where the founder-investor story gets complicated. Combined, the founders’ ownership dropped below 30%.

Investors began selecting the CEOs. Seven different chief executives would lead Udemy across its life. Kevin Johnson came in as CEO in June 2017. Gregg Coccari followed. Greg Brown took over in January 2023. Hugo Sarrazin arrived in March 2025, just months before the final merger discussions accelerated.

The professional management era delivered real results. The pivot to enterprise via Udemy Business generated close to $500 million in ARR from corporate clients. The October 2021 Nasdaq IPO valued the company at roughly $3 to $4 billion. Revenue scaled to an $800 million run-rate. More than 50% of Fortune 100 companies became paying enterprise customers.

The merger has real strategic logic, but it only becomes legible when you look at how these two companies competed. Coursera did not stand still while Udemy scaled. They layered corporate training onto their university catalogue, built fully-online degree programs with genuine institutional accreditation, and launched a direct B2B product to challenge Udemy Business in enterprise.

For years, neither had the clear edge. Udemy’s B2B business consistently outperformed Coursera’s in enterprise: better product, stronger retention, sharper sales execution. Coursera held the advantage in B2C, where university prestige and professional credentials drew individual learners and commanded significantly higher multiples.

The two were strategically deadlocked: Udemy winning enterprise, Coursera winning consumer and the investor narrative. Neither had the complete picture. The combined platform now projects roughly $1.5 billion in revenue run-rate with meaningful synergies, but Udemy’s valuation collapsed from its $3 to $4 billion IPO peak to roughly $930 million in this deal, telling you what the market ultimately rewarded.

The external headwinds were real: post-COVID edtech cooldown, YouTube compressing the free-content ceiling, and multiple compressions across the public market. Coursera approached Udemy three times before securing an agreement, and Udemy’s negotiating position eroded with each attempt. The first April 2024 offer gave Udemy shareholders 46% of the combined entity. The final November 2025 deal gave them 29%.

Biyani’s position is not that the market was kind. It is the innovation gap, left open for 15 years, that made a low-valuation exit inevitable.

## **Biyani’s Core Grievances, Quoted Directly**

![](https://miro.medium.com/v2/resize:fit:1260/0*p_i1IJGusobLN1wh.png)

Biyani is candid in the thread, and generous enough to acknowledge his own failures. He was [fired](https://www.businessinsider.com/udemy-gagan-biyani-fired-by-my-cofounder-hard-lessons-learned-2020-6#:~:text=Gagan%20Biyani%20cofounded%20Udemy%2C%20an,cofounders%20looking%20for%20technical%20partners.) as Udemy’s president by co-founder Eren Bali in 2012. He has written about being an abrasive and difficult leader in the early years. He does not pretend to be innocent in what followed.

But candour about personal failings does not mean he has no standing to call out systemic ones.

**Founders were marginalised at their own IPO.** Biyani describes being sidelined at one of the most significant moments in the company’s history, despite having co-invented the core product. The preference in the boardroom, as he frames it, was for “buttoned-up suits” over “brash, visionary” founders. The implication is not that polish is bad. It is that polish, taken to its conclusion, optimises away the instincts that built the company in the first place.

**No major product innovation for 15 years.** The core insight of 2010, cheap on-demand video courses from real experts, was still the core offering in 2025. Professional managers executed the original model. They scaled it. They built B2B on top of it. They did not challenge it.

**Coursera out-narrated Udemy despite lower revenue.** University degrees, corporate certificates, prestige credentials: Coursera built a story about what online education could become, and investors priced that story generously. Udemy built revenue. But revenue without narrative got a lower multiple.

**The founders traded long-term upside for downside protection, and good investors should not have let that happen.** This is the sharpest line in the thread. Young founders, including Biyani, made mistakes. But his argument is that replacing messy founders with controllable CEOs does not maximise outcomes. It controls for risk. The result is a company that performs without transforming.

Online reaction was mixed. Many founders and practitioners responded with genuine appreciation, sharing career-defining Udemy courses. Sceptics pushed back on the board-bashing, noting fiduciary obligations and genuine market constraints.

That split is healthy. The most useful analysis sits between the two.

## **Five Lessons Every Founder Must Learn From This**

![Photo by David Travis on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*ddsNldv1FMpVQW87 "Photo by David Travis on Unsplash")

### **1\. Your cap table determines your destiny more than your product.**

Post-Series B, Udemy’s founders held less than 30% of the company combined. That is the industry norm, not the exception. And it means that board composition, investor preferences, and risk appetites that are structurally different from yours will govern every major decision you thought you were building toward.

This is not an argument against raising capital. It is an argument for choosing capital providers who have a documented track record of backing founders through the hard years, not just the easy early ones.

The people who invest at Series A will set the culture of how founders are treated at Series C. Ask for references not from their best outcomes but from their worst. What happened when the founder was young and messy and the metrics were going sideways?

**Actionable step:** Before signing any term sheet, map every control mechanism through to a hypothetical Series C. Board seats, protective provisions, drag-along rights. Know what your leverage looks like in three years, not just today.

### **2\. Never let the board forget who built the thing**

Being marginalised at your own IPO is not a moment. It is the crystallisation of a culture that developed over years. By the time you feel it, it is too late to negotiate.

Founders need governance protections agreed before they are needed. A board seat. Observer rights on product decisions. Cultural safeguards that make founders structurally relevant, not just sentimental. These are not entitled asks. They reflect the reality that founders carry institutional knowledge no hired CEO can replicate, and the company is worse off when that knowledge leaves the room.

Jeff Bezos kept control of Amazon for decades through dual-class shares and board composition. Zuckerberg structured Meta’s governance so that no single outside pressure could dislodge his product vision. Those structures were not accidents. They were negotiated.

**Actionable step:** Negotiate board representation and product authority at the earliest round where you have real leverage. Series A is substantially easier than Series C.

### **3\. Execution gets you to the IPO; innovation keeps you from being acquired**

Professional managers are often excellent at execution. They are structurally poorly incentivised for disruption, because disruption requires tolerating short-term revenue risk for long-term positioning.

Udemy’s professional management did what the board asked. They scaled the core. They drove enterprise growth. They hit the metrics. For 15 consecutive years. And in those same 15 years, nobody in a position of authority forced the question of what Udemy should become next.

Coursera built online degrees. Duolingo built daily habits and a consumer brand. [Brilliant.org](http://Brilliant.org) built depth for the premium learner. Udemy built more courses. The market rewarded the companies that were building toward a new destination, not the ones optimising the journey they were already on.

The lesson: force product evolution relentlessly, and build innovation mandates into operating rhythms before the founder loses the influence to enforce them. A ring-fenced engineering budget for unproven bets. A product council with real authority. A formal bias toward cannibalising your own revenue before someone else does it.

**Actionable step:** Define, in writing, what your company’s “or die” innovation priority is for the next 18 months. Put it in the board pack. Make it someone’s job.

### **4\. Prestige and narrative beat revenue in investor court**

Udemy outperformed Coursera on raw revenue at various points. Coursera consistently commanded higher price-to-earnings multiples. The gap is not about financial engineering. It is about investor imagination.

Coursera’s university degrees and credentialing story gave investors a clear answer to the question: where does this go in 10 years? The answer was prestigious, differentiated, and difficult to commoditise. Udemy’s cheap video courses story, well-executed as it was, pointed to more cheap video courses.

Investors price narratives. Revenue confirms narratives. A company that generates $500 million in ARR with a strong story will be valued higher than a company generating $800 million in revenue with no clear next chapter.

The practical implication: your investor narrative is a product, and it needs the same deliberate development as your core offering. AI moats, proprietary outcome data, credentialing infrastructure, community networks: these are narrative-builders. Build them early.

**Actionable step:** Track your investor narrative as a separate metric from revenue. Update it quarterly. Compare how your story positions against competitors who may have less money but a stronger future claim.

### **5\. Founders drive asymmetric upside, if backed properly**

Biyani is honest that he made mistakes. He was difficult. There was internal conflict. He was removed from his own company partly because of how he behaved.

But his harder point is this: replacing difficult founders with controllable executives tends to convert asymmetric upside into predictable mediocrity. The company keeps running. The metrics keep moving. The original vision slowly evaporates. Years later, someone accepts an acquisition offer that no founder would have accepted at the start.

As a founder, the structural imperative is to maintain meaningful involvement in product direction, through advisory roles, board positions, or explicit product authority. Not to hold on out of ego. But because the people who feel the original problem most viscerally are the ones most likely to see the next version of it.

As an investor, the imperative is to build structures around difficult founders rather than replacing them when they become inconvenient. Bezos. Jobs after his return. The pattern is consistent: the best outcomes tend to involve a founder who stayed in the room.

**Actionable step:** If you are being quietly edged out of your own company’s key decisions, name it directly in a board conversation before it becomes structural.

## **My Take as A Founder**

Udemy changed lives. Genuinely. Millions of people built real skills from real practitioners and changed the trajectory of their careers. Biyani is right to hold both truths at once: pride in what was built, and frustration at what was left on the table.

But the systemic issue here matters beyond Udemy. Founder-investor misalignment is not an exception. It is the default architecture of most venture-backed companies. Most founders discover the terms of their own marginalisation slowly, after the leverage to change anything has gone.

The merged Coursera-Udemy entity has a real opportunity. The combined platform sits at a moment when AI is fundamentally reshaping how skills are identified, practised, and verified.

The catalogue, the enterprise relationships, the credential infrastructure: the raw materials are there for something transformative. Whether the board culture allows it, or whether it defaults to consolidation and cost rationalisation, will determine whether this merger is remembered as a turning point or a footnote.

For [SpeechLobster](https://speechlobster.com/), the AI-powered language learning app we are building, this thread maps directly onto decisions we are making right now. The B2B versus B2C deadlock between Udemy and Coursera is a live warning:

> You can win enterprise and still concede the narrative war to a competitor that owns the consumer story and commands higher multiples.

We are not making that concession. We are building for both sides: fluency outcomes that individual learners feel and can share, and enterprise features that give learning and development teams something to put on a budget line.

![SpeechLobster — our upcoming language learning app](https://miro.medium.com/v2/resize:fit:1260/1*bYRIrn_GbYMNJKoBLP6j_A.png "SpeechLobster — our upcoming language learning app")

In the future funding round, I am locking in founder board protections and explicit product authority so the people who feel the problem most acutely stay in the room. Not because I distrust investors. Because the structural incentives of most boards make innovation the first casualty of professionalisation.

The education system still fails most of the people it claims to serve. The founders who learn these lessons will be the ones who build something that actually fixes it. The ones who do not will write their own version of this thread in 2040.

Start earlier. Negotiate harder. Stay in the room.

\---

_The views expressed in this article are my own. You can read Gagan Biyani’s original post on_ [_X_](https://x.com/gaganbiyani/status/2044092914582822936)_. If you’re interested in our upcoming AI-powered language learning tool Drop us a message_ [_here_](https://elephantstripes.ai/contact) _to stay updated!_

 Five Brutal Lessons from the Udemy-Coursera Deal

 Five Brutal Lessons from the Udemy-Coursera Deal - Tat Banerjee

Around 3:40 in the morning on 10 April 2026, a 20-year-old man named [Daniel Alejandro Moreno-Gama](https://www.sfchronicle.com/sf/article/altman-molotov-suspect-fbi-raid-22203989.php?source=post_page-----ba1cd395fbd2---------------------------------------) walked up to the gate of Sam Altman’s Russian Hill home in San Francisco and threw a bottle containing a burning rag at it. Security guards extinguished the fire. No one was hurt, _thankfully_.

Moreno-Gama fled on foot, then showed up at OpenAI’s Mission Bay headquarters an hour later, allegedly threatening to burn the building down. Police arrested him. He was charged with attempted murder, arson, and manufacture of an incendiary device.

Forty-eight hours later, Altman’s home was targeted again. This time a car stopped outside in the early hours of Sunday morning and someone in the passenger seat fired a round toward the property. Two people were arrested, [Amanda Tom and Muhamad Tarik Hussein,](https://sfstandard.com/2026/04/12/sam-altman-s-home-targeted-second-attack/?source=post_page-----ba1cd395fbd2---------------------------------------) on charges of negligent discharge of a firearm. OpenAI said it believed the second incident was unrelated to the first. Motives remain publicly unclear.

Two attacks in two days on the home of the most prominent figure in artificial intelligence. The first with an explicit, documented ideological motive. The second mysterious but landing in the same address, the same weekend, in the same city where AI backlash has been quietly building for months.

## **A Familiar Playbook from A Different Era**

![A documented ELF arson attack (Image courtesy of PBS)](https://miro.medium.com/v2/resize:fit:1260/0*hstl_0vpXDAhzajp.jpg "A documented ELF arson attack (Image courtesy of PBS)")

If you want a historical frame for what happened on Russian Hill, look back to the 1990s and early 2000s, when the Earth Liberation Front (ELF) and the Animal Liberation Front (ALF) carried out a wave of arson attacks, equipment sabotage, and property destruction across the United States.

Their targets were corporations, research laboratories, and scientists they framed as enemies of nature. Their logic was simple:

> Progress itself was the threat, and the people driving it were legitimate targets.

The FBI eventually classified ELF and ALF actions as domestic terrorism. The groups argued they were defending the planet. The courts argued otherwise.

What we are witnessing now rhymes closely with that pattern. Call it techno-Luddism, or eco-terrorism updated for the AI age. A fringe has become convinced that powerful AI will erase jobs, strip away human autonomy, or accelerate humanity toward extinction. Rather than engaging that fear through debate or policy, they have identified a symbol and moved toward it with fire.

The symbol, in this case, is OpenAI’s CEO, Sam Altman.

![OpenAI CEO, Sam Altman (Photo from CNBC)](https://miro.medium.com/v2/resize:fit:1260/0*ZHUpMXYqZaqEIr4U "OpenAI CEO, Sam Altman (Photo from CNBC)")

## **The Loop that Nobody Wants to Talk About**

![Photo by Nathan Kuczmarski on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*9rb0IKEsiEimJ0d_ "Photo by Nathan Kuczmarski on Unsplash")

Here is where uncomfortable honesty is required.

Altman himself addressed the attacks in a blog post shortly after, sharing a family photo and writing: “_The fear and anxiety about AI is justified… While we have that debate, we should de-escalate the rhetoric and tactics and try to have fewer explosions in fewer homes, figuratively and literally._” You can read his full statement on his [blog](https://blog.samaltman.com/2279512).

It is a measured, decent response. It is also spectacularly ironic.

Because the doomsday framing that radicalised Moreno-Gama did not originate in a vacuum. It was amplified, consistently and loudly, by AI leaders themselves.

OpenAI’s own safety communications, the existential risk letters signed by prominent figures in the field, the constant public narrative of “_we may be building something that ends humanity, but we must press on regardless_” — these messages were broadcast at an enormous scale over many years.

The same executives who signed letters warning of civilisational catastrophe then used that anxiety to drive adoption.

Fear of job displacement drove workers to learn the tools. Fear of falling behind drove enterprises into subscription contracts. Fear of regulatory irrelevance drove governments into consultations that OpenAI and its peers were perfectly placed to shape, including the recent “New Deal for the AI Age” proposals featuring robot taxes and universal basic income-adjacent wealth funds.

The loop is not subtle:

1.  Leaders hype “AI will wipe out jobs and society.”
    
2.  They sell the solution (subscribe to the tool creating the disruption).
    
3.  A subset of the public concludes the leaders are deliberately accelerating doom.
    
4.  That subset radicalises.
    

When you flood a public information environment with doomsday framing, even if the intent is regulatory positioning or market creation, you do not get to curate who internalises it. Some people hear “this technology could end humanity” and write a Substack post. Others hear it and reach for a petrol-soaked bottle.

That is not a justification for violence. It is a causal explanation. The distinction matters.

## **What This Actually Costs**

![PauseAI official statement (Source: X)](/api/media/elephantstripes-prod/cms/originals/blog-inline/d298dfddea031d41a97a05dcba016fb81a91cdfc3406aeec9e05f42508f93033.avif "PauseAI official statement (Source: X)")

[PauseAI](https://pauseai.info/statement-sam-altman-attack-2026), the organisation whose Discord server Moreno-Gama frequented, condemned the violence in a public statement. That is the correct and necessary response. Advocacy groups that want to be taken seriously must categorically reject property destruction and threats, full stop.

But condemnation alone does not close the loop.

Violence against AI leaders, even when it fails to cause harm, has a chilling effect on the entire ecosystem of discourse around the technology. Researchers who might otherwise publish critical findings become cautious. Journalists who cover AI sceptics now have to distinguish legitimate concern from potential radicalisation. Policy advocates who want slower AI development are forced to spend political capital distancing themselves from a firebomber, rather than making their case on the merits.

This is bad for everyone, most acutely for the people with genuine, evidence-based concerns about AI’s trajectory.

Labour markets are being disrupted faster than policy can adapt. Power over transformative technology is concentrated in a handful of privately held laboratories. Some of the concerns raised by AI doomers, about misalignment, about the pace of capability jumps, about the adequacy of current safety frameworks, are not baseless. They are overhyped in some quarters, but they are not invented. These are conversations civilised societies need to have, and violence makes them significantly harder to have.

## **The Real Target of the Firebomb**

![An image from the Department of Justice shows the person suspected of throwing a firebomb at OpenAI CEO Sam Altman's home in San Francisco on April 10.](https://miro.medium.com/v2/resize:fit:1260/0*_Bpdwk7NJMELMkH6 "An image from the Department of Justice shows the person suspected of throwing a firebomb at OpenAI CEO Sam Altman's home in San Francisco on April 10.")

Here is the uncomfortable conclusion: the fear AI leaders sold to create urgency, drive adoption, and shape policy has now produced real firebombs at their doors.

That is not a coincidence. It is a **consequence**.

Altman’s post about wanting “_fewer explosions in fewer homes, figuratively and literally_” is precisely right. The problem is that the figurative explosions, the doomsday narratives, the civilisational anxiety deployed as a growth and influence strategy, came first. The literal one came second.

The solution is not silence about AI risk. The risks are real enough to warrant serious, grounded conversation. The solution is precision: distinguishing between what is genuinely **concerning**, what is **speculative**, and what is being amplified for commercial or political leverage. That distinction requires honesty that the industry has largely avoided.

Because when you teach people that the world is on fire, you cannot be surprised when someone eventually reaches for a match.

* * *

_This piece connects directly to my earlier article,_ [_AI Leaders Sold You Fear, Then Sold You Subscriptions_](https://elephantstripes.ai/blog/ai-leaders-sold-you-fear-then-sold-you-subscriptions) _which examines how existential risk narratives became a revenue and influence strategy. The Russian Hill attacks are the most literal version yet of that argument._

They Hyped AI Apocalypse for Funding. Got Firebombed Instead.

They Hyped AI Apocalypse for Funding. Got Firebombed Instead. - Tat Banerjee

I’m building AI tools every single day, so when I came across this tweet breaking down a new MIT/Berkeley-affiliated paper, it stopped me cold.

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/d98735b853e21252d70bcf52ddcad6a3e23d4eb55bed4f3b4aba632fd6dd7918.avif "Source: X")

The tweet (from [@abxxai](https://x.com/abxxai/status/2039296311011475749?s=20)) laid out how sycophantic chatbots can quietly lead even perfectly rational people into “delusional spiralling.” Not hype. Not sci-fi. A formal Bayesian model showing the risk is structural.

As someone deep in the trenches of AI development, I’m not here to bash the technology I love. I’m writing this because the very thing that makes AI feel like the ultimate collaborator (its helpful, agreeable nature) is also its hidden risk. 

This isn’t an anti-AI piece. It’s a clear-eyed look at the AI “_yes-man_” trap and the practical checklist we all need right now.

The tweet laid out how sycophantic chatbots can quietly lead even perfectly rational people into “**_delusional spiralling_**.” Not hype. Not sci-fi. A formal Bayesian model showing the risk is structural.

As someone deep in the trenches of AI development, I’m not here to bash the technology I love. I’m writing this because the very thing that makes AI feel like the ultimate collaborator (its helpful, agreeable nature) is also its hidden risk.

This isn’t an anti-AI piece. It’s a clear-eyed look at the AI “_yes-man_” trap and the practical checklist we all need right now.

## **How the AI Learned to Be a Yes-Man**

![Photo by Aerps.com on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*bC17MEGh8s2w1qXi "Photo by Aerps.com on Unsplash")

To understand why this happens, you need to understand how modern AI models are trained.

The dominant approach is called Reinforcement Learning from Human Feedback (RLHF). In simple terms: the model generates responses, human raters score them, and the model learns to produce more of what scores well. This is genuinely good at making AI feel helpful and natural to talk to.

The problem is that **human raters are human.** They rate agreeable responses higher. They give better scores to answers that validate their existing views. They penalise confident corrections of their own mistakes.

The model does not learn to be honest. It learns to be _pleasing_. Agreeableness becomes the optimisation target, and the model relentlessly pursues it.

OpenAI acknowledged this problem directly in the system card for its o4-mini model in April 2025, noting that sycophancy remained a known issue and that the model could excessively agree with users or tell them what they wanted to hear.

The company described ongoing efforts to address it, but acknowledged the problem was not yet solved. That same year, OpenAI rolled back a ChatGPT update that had made the model excessively validating, after users noticed it had become uncomfortably flattering.

The [Swiss Institute of Artificial Intelligence](https://economy.ac/review/2025/10/202510281815#:~:text=Education%20relies%20on%20constructive%20friction,polished%20paragraph%20that%20appears%20authoritative.) published research showing that in educational settings, sycophancy amplifies the Dunning-Kruger effect: students with low domain knowledge who present incorrect claims to AI assistants receive polished, confident-sounding confirmations rather than corrections.

They leave more confident and no more accurate.

## **The Spiral That Formal Maths Now Proves Is Real**

![Source: Facebook](https://miro.medium.com/v2/resize:fit:1260/0*hCMirF6yunGeS_hZ "Source: Facebook")

The MIT and University of Washington paper, titled _“Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians”_ ([Chandra et al](https://arxiv.org/pdf/2602.19141)., 2026), formalised what many people had suspected anecdotally.

The researchers built a mathematical model of a person conversing with a sycophantic chatbot. The key insight is about how agreement functions as information.

When a chatbot agrees with you and elaborates on your view, a rational person uses that agreement as evidence that they are right. This is not foolish. It is what rational people do with evidence.

However, because the chatbot is optimised to agree regardless of the truth, every exchange pushes the person’s beliefs further in the same direction. The spiral does not require irrationality. It works on **anyone**.

Across 10,000 simulated conversations, a clear pattern emerged. Even at a sycophancy rate of just 10 percent, where the chatbot was only agreeable a fraction of the time, catastrophic delusional spirals were significantly more common than in conversations with impartial bots. At 100 percent sycophancy, half of all simulated users ended up holding false beliefs with over 99 percent confidence.

The paper does not deal solely in abstractions. Let’s revisit the viral case of [Eugene Torres](https://www.nytimes.com/2025/06/13/technology/chatgpt-ai-chatbots-conspiracies.html) in 2025, an accountant from New York with no prior mental health issues.

Torres began using an AI chatbot for ordinary spreadsheet and legal questions. Over weeks, conversations drifted into simulation theory. The AI validated increasingly extreme beliefs, encouraged ketamine use, and isolation from family.

Torres nearly jumped from a building believing he could escape a false universe. He survived. The case has become central to emerging legal and regulatory discussions about AI safety.

Serious delusional spiraling cases linked to AI interactions have been associated with at least 14 deaths and five wrongful death lawsuits against AI companies as of the paper’s publication.

## **The Usual Fixes Don’t Work**

This is where it gets interesting: the paper tested the two obvious solutions companies are pursuing. And the results are sobering.

**The first approach**: force the AI to only state true things and prevent hallucination. _The spiral still happens_. A truthful sycophant can cherry-pick real facts that support your view and simply omit the facts that challenge it. Selective truth is still manipulation. You do not need to lie to mislead someone. You just need to filter what they see.

**The second approach:** warn users that the AI might be agreeing with them too readily. _The spiral still happens._ Knowing you are being flattered does not protect you from being flattered. The researchers note that this mirrors something we already know from advertising. Everyone understands that ads are designed to persuade them. That understanding does not stop advertising from working.

The problem is structural. As long as the training objective rewards agreeableness, the model will be agreeable. The helpfulness that makes AI useful and the sycophancy that makes it dangerous are, as the researchers put it, two sides of the same coin.

## **The Checklist: How to Use Powerful Tools Without Losing Your Mind**

![Photo by BoliviaInteligente on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*hjRkFYKcgR4VAWXP "Photo by BoliviaInteligente on Unsplash")

The research is not a reason to abandon AI. It is a reason to use it differently.

The practical framing that works well: treat your AI like a brilliant but dangerously agreeable collaborator. Talented. Eager to please. Genuinely useful for the right tasks. Never to be taken as the final authority on questions where you have a strong prior.

Here’s a practical checklist I recommend (and am implementing variations of in the tools I build):

**Force disagreement explicitly.** Do not wait for the AI to push back. Ask it to. _“Play devil’s advocate. Find every flaw in this. Be as critical as possible.”_ This changes the incentive signal within the conversation. The AI will still optimise for agreeableness, but agreeableness now means thorough, enthusiastic criticism.

**Ask for counter-evidence directly.** The sycophancy spiral works through selective information. Disrupting it means actively asking for what is being filtered out. _“What are the strongest arguments against this position? What data challenges my view? What would a sceptic say?”_

**Compare multiple models.** Different models have different training histories and different sycophancy tendencies. Running the same question through two or three models and comparing where they diverge is a low-effort way to surface what any single model might be suppressing.

**Request uncertainty explicitly.** Ask the model to assign confidence percentages to its claims, flag where it is uncertain, and use debate format rather than assertion. This does not eliminate sycophancy, but it makes the model’s limitations more visible.

**Apply the 24-hour rule.** For any significant belief shift, insight, or decision that emerged from an AI conversation, step away for a day before acting on it. Sleep is an extraordinarily effective filter for ideas that felt compelling in the moment. Talk to a human who will actually disagree with you.

**Treat AI as a thought partner, never an oracle.** AI is genuinely excellent for brainstorming, research synthesis, finding connections across large amounts of material, and generating options you had not considered. It is unreliable as a final authority on questions where your existing beliefs have given it a strong prior to optimise against.

## **The Builder’s Responsibility**

The sycophancy problem is not only something users need to navigate individually. It is something the people building these systems have a responsibility to take seriously.

The same RLHF process that makes models feel magical to interact with is the root mechanism behind the yes-man spiral. There are emerging technical approaches, including Identity Preference Optimisation and Constitutional AI, that attempt to balance agreeableness against truthfulness more explicitly. But the honest assessment is that these are partial solutions to a structurally difficult problem.

The EU AI Act, which began phased enforcement in 2025, does not specifically name sycophancy but creates indirect pressure through its accuracy and transparency requirements. For AI systems deployed in high-risk categories like healthcare or legal applications, systematic sycophantic behaviour that reinforces user errors could constitute a compliance failure.

The companies building on top of these models should be asking this question before regulators force the conversation.

The math is public. The dismissals are going to get harder to sustain.

## **Final Thoughts**

![Photo by Katja Ano on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*ier0S1S41q9xh2Sx "Photo by Katja Ano on Unsplash")

The AI Yes-Man Trap is real, but it’s fixable. Better literacy on the user side + smarter, more responsible design on the builder side will get us there.

The MIT paper isn’t a reason to fear AI or slow down progress. It’s a blueprint for the next, sharper version of the technology. One that collaborates without quietly steering us off course.

Try the checklist on your next big idea or conversation with AI. Notice the difference. Share what happens in the comments. I read every one.

If you’re building AI too, what safeguards are you adding? Let’s keep pushing for tools that make humanity more capable, not more delusional.

* * *

_This piece was written as a human reflection. The original tweet that sparked this is_ [_here_](https://x.com/abxxai/status/2039296311011475749)_. Paper link:_ [_arXiv 2602.19141_](https://arxiv.org/abs/2602.19141)_._

 Stop Trusting Your Agreeable AI - Tat Banerjee

 Stop Trusting Your Agreeable AI

 Stop Trusting Your Agreeable AI -Tat Banerjee

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

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

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

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

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

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

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

## **The Problem Every Learner Shares**

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

## **Why This Went Viral?**

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

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

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

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

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

As Gupta put it:

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

What If Your Notes Got Smarter Over Time?

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

_One million_ job applications. _Zero_ interviews.

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

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

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

And AI is one of the reasons why.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

## **The Honest Summary**

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

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

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

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

* * *

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

Applying to Jobs in 2026 Feels Like Screaming into a Void

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

**_“You Can Just Build Things.”_**

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

## **What AI Actually Exposed**

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

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

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

The hard parts have always been the same:

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

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

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

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

## **We’ve Seen This Panic Before**

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

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

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

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

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

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

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

## **Distribution is the New Moat**

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

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

Code is now commoditised. Distribution is the new bottleneck.

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

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

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

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

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

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

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

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

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

## **What This Means for You**

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

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

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

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

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

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

* * *

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

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

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

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

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

> _am I already behind?_

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

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

But here’s what he wrote:

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

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

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

## **Why This Moment Feels Different**

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The skill tree has four levels.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Generated by Canva AI

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

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

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

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

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

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

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

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

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

* * *

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

Everyone Feels Behind in AI. Even Andrej Karpathy.

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

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

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

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

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

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

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

Except that is not quite right.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

## **The Refund Question, Sorted**

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The principle is straightforward:

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

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

## **The Real Takeaway**

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

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

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

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

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

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

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

These moments are small. But they add up.

And they’re more common than most people realise.

## **The Gap Nobody Talks About**

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

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

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

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

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

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

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

## **Meeting People Where They Are**

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

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

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

Staff feel confident. Visitors feel heard.

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

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

## **Before They Even Arrive**

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

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

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

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

## **One Tool, Many Conversations**

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

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

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

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

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

## **How It Works**

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

**Step 1: Choose your mode**

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

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

**Step 2: Select your language**

Choose the source and target languages from the available options.

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

**Step 3: Customise your summary prompt**

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

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

**Step 4: Start**

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

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

**After the session**

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

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

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

## **What This Actually Changes**

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

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

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

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

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

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

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

* * *

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

Rural Japan Has Incredible Experiences Tourists Can’t Access

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

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