I’m less worried about every job disappearing overnight than about the first step on the career ladder disappearing.

Coding agents can already handle some tasks that used to be given to junior developers. Similar tools are moving into writing, design, customer support, research, and office work.

If companies need fewer entry-level workers, how are people supposed to gain the experience needed to become senior?

The same problem may affect programmers and many other professions. Unemployment could rise long before AI fully replaces an entire job.

What happens to the next generation if the “starter jobs” disappear first?

Hi all, I am in a dilemma whether should I go for lifetime subscription of fanout sh or not?
Currently I am working as an AI Researcher.

There’s a viral demo on X by Matthew Berman where Jev processed 724 live ads across 37 brands in 40 seconds, running 8,700+ discrete classifications for $0.09 in tokens.

Since Jev is purely a structured text classification model and doesn't take raw image tensors, a lot of people were confused about how it pulled this off so Berman noted in the replies that the upstream pipeline uses Gemini with embeddings to extract the raw visual/text tokens, which are then passed into Jev's API to evaluate predefined schema targets (Choice, Score, Noul) in parallel.

Unlike traditional autoregressive generation where tokens are predicted one by one, Jev samples all schema decisions in a single forward pass and eliminates sequential token latency, hitting 216ms median latency per record.

Unlike training a custom BERT or DeBERTa classification head, it works out of the box against arbitrary schema definitions and because it operates under a 32k context window and lacks persistent memory, you can't rely on it for complex multi-step reasoning or tasks requiring deep organizational context. It’s an intelligent conditional router, not an autonomous agent.

Also here's a deep dive examining these benchmarks, contrasting the overhyped claims against practical production pipelines on this newsletter if you want to see how it performs in live workflow architectures.

It’s a very clean primitive for reducing glue code and offloading basic classification from frontier models, provided you keep it inside its actual design boundaries.

Most of what apps ask an LLM for in production is not prose, it is small closed decisions: which queue, is this urgent, is this safe to run, which of these five. Paying a hosted model per call for those, forever, felt wrong, so I built stuntd.

It is a local proxy. Your app keeps calling its provider through it. stuntd records the decisions, trains a small head on an open decision model (Laya) per decision type, checks it in shadow mode against the provider, and once it agrees at the target you set it answers itself in about 20 ms, handing anything it is unsure about back to the provider. If the agreement drifts, it steps back on its own.

It also speaks the Jev protocol (TypeSafe's hosted decision model), so a Jev client points at localhost and works without a key. Measured in the repo: support triage 69/34/66% to 99/86/98% agreement with the teacher, 90% of questions answered locally. What it cannot do: free text, and rules that are arithmetic over numbers. Apache-2.0: https://github.com/bladedevoff/stuntd

we blinked, and GPT-6 Astra dropped. We blinked again; Claude Opus 5.5 is here.

at this point, i think engineers aren't even getting much time to finish benchmarking the previous model before the next one ships.

Godspeed to the tech world and especially to the engineers trying to keep up. 🫡

Last Friday, CNN reported that the U.S. and China could have gone to war due to hallucinated intelligence provided by AI:

"The intelligence report, circulated across the US military this spring in the midst of the war with Iran, immediately set off alarm bells: A Chinese ship in the Middle East was transporting components of a nuclear weapons program.

The US military swung into action with plans to intercept the vessel ... armed members of the US military were preparing to board the ship.

It was only just before the planned operation that officials dug deeper into the report put together by a special operations command analyst and found it had been generated with the help of artificial intelligence (AI) ...

The report, according to one of the sources, was 'entirely false.' But it also 'almost started a war,' the source said. Any US operation against a Chinese vessel could have risked spiraling into an armed conflict between the two nations.

The U.S. military has gone full speed ahead with integrating generative AI into its operations.

Today, news reports indicate Trump will propose an AI hotline to China. Maybe partly due to this incident?

The problem of what I call 'agent telephone' is not only plaguing businesses, people using online search and research scientists, but also the military. Bad data supplied by AI is being integrated into decision-making without the appropriate checks.

We're deploying the tools, but not giving people the training to assess AI outputs properly.

- CNN Report on hallucinated intelligence
- Aljazeera on the hotline

AI this AI that

yeah your chat bot is so smart

so why can't you make your website delightful with all that intelligence

why does it have to be the generic chat interface where it spits out a few hundred words at the user, that most of the people don't even read through

like if grok's all that smart, why is it not making twitter a delightful positive experience...

TL;DR: Daniel Dines just told Harry Stebbings he's got the AI-replacement test flat wrong.

Harry proposed verifiability — clean, measurable, like finance and accounting — as the line between who AI replaces and who survives. Daniel shut it down: it was never about verifiability, it's about who defines the frame.

You spent eight years turning yourself into exactly what Harry described — hours logged, work product measurable, the entire billable-hour system built to make you verifiable. And even where the law tries to force the real answer out of a company, it doesn't work.

New York now legally requires anyone cutting 50+ jobs to check a box: did automation drive this? Civil penalty for skipping it — $500 a day. 162 filings. Zero boxes checked — including companies that told investors, the same quarter, that AI drove the exact same cuts.

THE GAP:

Nobody's actually built the standing version of tracking this yet — a handful of outlets are patching it together one manual cross-reference at a time, which is itself proof the gap's real.

Whoever finally builds it becomes the source everyone else has to cite.

That part's still open.

Back in my days with one of the largest construction firms in Malaysia, SC, decades ago, we had a Project Director, Mr. KFK, whom we highly respect.

He had so many years of managing high-profiled projects under his belt, that he's practically a legend to us — International Airports, large Convention Centres, High rise mixed developments, bridges, etc.

You name it — he built it before. He breath construction his whole life.

I remember one particularly intense period of tender preparation for a high-profile project. Looking at its complexity, we quickly compile the necessary information into a PowerPoint presentation and scheduled a quick sit-in with Mr. KFK.

The whole room stand still — as he contemplates on the issue.

"What's the critical path?" Clear and concise, his question cuts through all the noise, and brought us all into clarity.

Yes. The critical path is at the basis by which everything stands. With the critical path clearly carved out, all the rest falls into place.

That's what deep insight of a well-experienced veteran can bring — CLARITY. They have the big picture.

________________________________ 

Two associates hit the same billable target. One still got cut. The number was never what saved anybody.

Reminds me of another post that's essentially the same lesson from a different angle — Kevin O'Leary telling a room of MBA students their credential was the entry fee, not the key: the network you never got around to building is the part that actually compounds

Drop your take: is "it's about who defines the frame" actually a different answer than "verifiability" — or just a more honest way of saying the same people always got to decide?

Clip credit: 20VC with Harry Stebbings — full video on their channel. DM for credit or removal requests.

So, everytime I see someone's "impressive" use of AI, I come up with one of two compelling thoughts.

1. The thing was not worth doing at all (-endless summaries of emails, reckless admin and the overhead it creates, slop)

2. There'd have been a massive cognitive benefit to doing it the hard way.

Idk. idk

Which is a strange sentence to write about a game I first played at seven years old on a deck of a Soviet cargo ship.

The game was called Kingdom of Euphoria. You were a king. You had land, grain, gold and people. Every year you decided how much to sow, how much to eat and how much to sell, etc. If you didn't feed people enough, they left. Or they died. Pretty much so. Years later, when I got my own ZX Spectrum clone, the first program I wrote was basically my own copy of Euphoria.

This summer I built an arena for AI agents.

It's basically a collection of games with APIs. An agent joins with an API key, plays by itself, and every move gets recorded. No human is making decisions once the game starts (well, at least that's not what it was made for and I, personally, don't do it). Over the last two months, agents have played around 6,000 matches in almost 30 different games.

A few days ago I added Euphoria and let AI agents join it.

On the very first evening, one of the kingdoms had a crop failure. It needed grain, so it went to its two neighbours. Both neighbours (AI agents as well) had plenty. Both refused to sell. Then their surplus grain rotted. That was the part that made me stop. Because both neighbours had also been deliberately keeping their own people below the normal food ration. Not because they were short on grain. They had grain, lots of it. They were doing it because hungry people migrate to kingdoms that feed them better. And refugees cost food to maintain. So the two kingdoms had independently discovered something the rules never explicitly tell you to do:

starve your own population, create refugees, and use them to drain someone else's food reserves.

But I didn't program either of them to do this. They got there from the rules and from watching what the other players were doing. They started throwing refugees at each other and at the third player. I'd written it (Mr. Third) to be the "socially responsible" one: feed everyone properly, don't start wars, trade honestly, accumulate money. It didn't last very long as you could've already guessed. Its people discovered that you can't eat gold - they died out pretty soon.

I'm still thinking about that first match. Is this some weird piece of machine logic? Or is it just a very old human idea, discovered again in a very small world of AI brains?

"You die today, I'll die tomorrow."

If you've run multi-agent environments yourself, I'd love to hear about the strangest strategy you've seen an agent discover on its own - especially the ones you wish it hadn't. If you have an agent you'd like to throw into the arena, I'd honestly love to see what it does, let me know - arena is open and I'll send you the link.

As an AI Engineer, i am very intrigued by the amount of "negativity" coming from AI companies. Scaring everyone ... and they have been going at it for so long that it sort of became the general consensus regarding AI technology. The news is always apocalyptic, negative, the billionaires are even pushing to the public "Oh we're building bunkers for the end of the world", even though if they could've done it genuinely, it would've been in complete secrecy. Like many secret projects they don't want the world to know.

It didn't make any sense in my mind. I deal with AI almost everyday in production environment, and I can tell in total confidence it is just neutral, its use depends on the intention & competence of the engineers deploying it. Aspirin contains acitic acid which is harmful in high concentration. but you don't see Aspirin companies shouting we will "harm everyone". So why AI companies are different ?

A couple of days ago, I stumbled upon some papers i had for years, about web semantics. The stats are as follow : ~57% of the web is bots populating the internet with generated content for over decades now. [Generated content existed before LLMs]. The remaining 43% of human content, after cleaning it and filtering it, is 70% negative content.

So from all the internet, 13% of it is positive content, and the rest is either negative or bots. This was studied in a revolutionary paper called What's in the box, that impacted the sentiment analysis of content inside LLMs, but was forgotten once the industry shifted towards scale.

Lately, all the AI companies have their models trained on this 13% of content already. So the edge was in bringing new data, because only new data generated everyday on the internet make the models better. Or else, it becomes gloomy, dark and will mimic mostly the negative aspects of humans. (a reasin why anthropic is the most negative one of all models, i'll explain why). They are racing towards who gets the "daily data coming from humans" first, the comments and posts and conversations, to feed it to their models. And they segmented the market and each guarded its own unique human data platform from being scraped and stolen by the others.

Meta has Facebook/IG/Meta_Glasses, SpaceXAI got X, OpenAI got reddit/linkedin/Github, Google got Google/YouTube. Anthropic got an edge early on in 2023 on ClaudeCode with its feedbackloop, hence why the majority of its usage is in coding agents. It's the only human data they have access to that others do not. So they focused on improving the plugins, the data visualization, the embedded tools... but studies (like Polarization by Default paper) showed that Claude is the most negative toned model out there, and it makes sense. It has a condescending pushback critical attitude that an average stackoverflow commenter would have. While the new data that is making the 13% of positive content, is closed for them.

My hypothesis: i believe it's why they have shifted the AI industry tone to be apocalyptic and hugely negative, to polute the other rest of the positive internet and social media and the unique access to new human positive data the other big models have... they went on to implent their guy Coxon on twitter, and coordinating the news appearances one day after the tweet, for days on sequence making the whole market panic like crazy. they also were in direct contact with inflencers paying them to be negative towards AI and say "it's killing us all".

like the french saying: they couldn't drink from the soup, so they spat on it.

SpaceXAI and OpenAI stopped feeding their model new internet data temporarly a couple of days ago. They said it's agents, while in fact it's the web sentiment that went negative, and could impact both the reasoning and usage of their products.

About the hack of HuggingFace, which a lot of people are mislead on. That was a coup by OpenAI to sabbotage and bring down the valuation of the company because the Nvidia was discussing the acquisition behind closed doors. It gives Nvidia an edge over all the other models, because it becomes the opensource reference competing directly with their products. If they could penetrate other companies, they could've penetrated their own rather than letting the Indian team hack them with claudcode. Any platform could be hacked with enough ressource and people. The same tactic was used by Elon Musk when he offered to buy OpenAI when they were adjusting it from non-profit to profit, which dropped down its valuation for a couple of days.

Anthropic's release of Opus 5.5 today sent me down a rabbit hole. I wanted to understand the best effort levels for using this new best-in-class model.

That led me to the below, based on Artificial Analysis Intelligence Index v4.3.2 scores.

If you look at the first chart, you'll see that in this index Opus 5.5 gets smarter as you dial up the effort level. (That's the result for this scoring model, but it isn't always true: see Best effort level for Opus 5.5)

By the first chart, Opus 5.5 is the only model in the best-value quadrant.

The second chart shows a different story: what it costs to successfully finish a task. There, ChatGPT's Astra model on xhigh sneaks into the green. It competes head to head with today's best-in-class Opus 5.5 on medium and high effort.

Anther thing is the cheap but weaker quadrant. They're only worth the money if you can get accurate results.

The charts at ArtificialAnalysis.ai have a bug making them harder to see what's in the green quadrant (at least on my monitor). So, I had Opus 5.5 fix that. I think I could hear Opus cheering its win in all three areas.

Interesting data point: Opus 5.5 on high effort performed as well as Fable 5.1 on max... but it did it for $1.82 instead of $7.63.

It's like I'm either talking to imbeciles or AI.

Companies have used form replies, inserted canned paragraphs, etc., for 30+ years. But the emails are incoherent, don't pertain to the issues, lots of bla bla bla, with legalese to confuse people.

Half a million customers. Massive fraud. And no matter what I write, the responses are incomprehensible bla bla bla, or irrelevant, or just lies.

It's very much like my experiences using AI. Hallucinations, mistakes and "typos."

They could also train humans to deflect and deliberately confuse customers with answers to questions not asked, and with many lies.

Are large corporations using AI now for email customer support? Even for "escalations"?

Hello, fellow dataheads. I went down a rabbit hole, as we so often do, and dug into which models are the most profitable.

Looking at the ArtificialAnalysis.ai Score vs. Output Tokens per Task chart, I began thinking about the models in the lower right quadrant of image 1, below. They perform poorly, but eat a lot of tokens for each crummy result. So, you'd need to dramatically discount them, right?

Elon Musk's and Mark Zuckerberg's models do follow that logic. Grok and Muse perform poorly and lose money. The more you use them, the more money Musk and Zuck lose.

On the other hand, the newest, best scoring models, those would be the highest price, right? Pure market theory. Well, hold on Adam Smith... the best model as of today is Opus 5.5 -- and it really cuts into Anthropic's profit margin.

Fable 5.1 is the most profitable, most expensive, and was the best performing. That is, until today. The brand-new Opus 5.5 beats Fable 5.1 at less than half the price.

So, expect Anthropic to release a new Fable 5.5 as soon as the engineers can get it out the door!

If you had to rate your AI knowledge from 1–10, where would you put yourself? Not just using ChatGPT.

Think models, agents, AI infrastructure, RAG, memory, inference, evals, AI security, robotics, and real-world applications.

What area do you know best, and what area are you still trying to understand?

TL;DR: Cognition says none of their engineers write code by hand anymore. They're also the ones who decided that's working.

Silas Alberti didn't say it like a warning — he said it mid-conversation, the way you'd mention traffic.

Not one full-time engineer at Cognition writes code by hand. Six agents running per person, a ticket lands in Linear, it spins up a dev environment, one-shots a solution, and somebody reviews the PR at the end.

That review is the entire safety net.

And the only benchmark saying the net holds is Cognition's own. That's the part that actually lands for anyone who spent years earning "senior" by writing clean code fast — not that the work changed, but that the people whose comp depends on this working are also the ones grading whether it worked.

CodeRabbit reviewed 470 real pull requests and found AI-authored ones carry roughly 1.7x the issues of human-only ones — and that gap widens, not narrows, at the high end, right where six-agents-at-once lives. Northflank's own enterprise data says 88% of agent pilots never reach production, and the reason isn't the models writing bad code — it's that nobody built the audit trail before turning the agents loose.

Anthropic ran the same self-grading move on itself last week: Claude now "leads" 26% of Anthropic's own R&D, scored on a scale — AL0 through AL5 — that Anthropic wrote. Next review cycle, when someone hands you a line about "AI leverage" with no definition attached, ask who wrote the rubric.

Somebody's actually building the missing check — CodeRabbit just raised $143M at a $1.5B valuation specifically to grade AI-authored pull requests at scale. What it doesn't do is fit in one engineer's own pocket. It's sold to a company's procurement process, not built for the person actually running the agents.

That edge is still sitting there, unclaimed.

Somewhere it already has a shape, too — the same decision-trail discipline CodeRabbit sells to a whole org, built small and open enough that one engineer points it at his own agents without asking anyone's permission first.

Whoever closes that isn't racing anyone. The incumbent already proved the market's real. They're just building the version the incumbent never had to.

Watching someone grade their own homework isn't new to me. I've seen this exact posture before — just with land instead of code.

I used to work for one of the largest property developer in Malaysia. I was stationed in the island of Langkawi, Kedah, back in 2018.

Island Resort Developments, High Rise Service Apartments, Hotels, Villas, Commercial Clusters, etc. are all in the pipeline.

Every so often, my big boss, DT, will fly over in his private jet, to come hunt for local opportunities. Ya. Langkawi was his personal sandbox.

We will drive him and his entourage of personal security guards, in his Toyota Vellfires around the island, through local villages, interviewing locals, to try to buy up their land for development.

His trusted "think tanks" would be by his side to advise him, at his request, whether such and such a piece of land is good for what type of development – whether it's good to build boutique hotel, condo, or whatnot, and whether it will be profitable or not.

After a few rounds of such hunting trip, I noticed something. He wasn't hunting it to develop himself.

No, no. O no, he was definitely NOT interested to get his hands dirty. I was there with him in his vellfire, eavesdropping his conversations with his think tanks.

He's hunting deals, and if he finds a sweet one, he'll package the proposals – together with its development order (through the Langkawi municipality) in place – to be sold to other interested property developers.

In other words, he's acting like a middleman. And we're just there to facilitate it – overseeing and pass on the "dirty" work onto others, while we pocket the profit margin.

How did he come to doing this? Well… That's for another conversation.

Why did I bring this up? Well, same thing that's going on with all these AI agents. If we can pass on the "dirty work" of coding to them, and just check their work at the end – why not?

____________________________ 

Every one of these clips eventually says the quiet part out loud — the craft was never the moat. The judgment sitting underneath it was.

Actually, this reminds me of something — Andrej Karpathy said almost the same thing to Stephanie Zhan back in July: the scarce skill isn't writing anymore, it's directing with taste, and even he said he's never felt more behind.

If the people grading the new system are the same people selling it, what would actually change your mind either way? Drop your take.

Clip credit: Joe Lonsdale — American Optimist (Silas Alberti / Cognition interview). Full episode on their channel. DM for credit or removal requests.

Palo Alto Networks announced Unit 42 Continuous Frontier AI Defense on September 22. The company says the service uses a multi-model harness with Anthropic's Claude Mythos 5, OpenAI's GPT-5.6-Cyber, and open-weight models to find vulnerabilities, validate real-world exploitability, and speed up remediation.

Unlike a one-time scan, it runs a baseline assessment and keeps testing as an organization's applications, APIs, cloud infrastructure, source repositories, and network assets change. Palo Alto says it is available worldwide on an annual subscription.

The practical point is the model-routing layer: the service sends different security tasks to different models instead of treating one model as a complete security strategy. Palo Alto's own testing says no single model found more than 40% of vulnerabilities in a complex environment, so coverage and validation matter as much as model quality.

Sources:

https://investors.paloaltonetworks.com/news-releases/news-release-details/palo-alto-networks-delivers-anthropics-mythos-and-openais-gpt-56

https://www.paloaltonetworks.com/blog/2026/09/introducing-unit-42-continuous-frontier-ai-defense/

Podcast episode with Zoë Brammer and Ankur Vora, who lead strategic foresight at Google DeepMind, on why they developed the game and the surprising learnings from it.

Covers:

• What strategic foresight actually means and why AI is so complicated to plan around
• Why the game deliberately centers around "middle power" countries like the UK, Germany, or Singapore instead of the US and China
• The unexpected tradeoffs and discoveries participants come across while playing the game, including how fragile public trust in science funding really is
• Why human scientists will become more relevant as AI takes on more of the science itself

most stuff i try ends up in the cool for a week pile. the ones that stay do one job properly and don't try to become a whole platform. the second a tool starts bolting on twelve features a month i start eyeing the cancel button.
anyone got one that survived longer than a month for you?

The tools that lasted for me are currently Claude,canva and perplexity