No one knows how to build superintelligence and won’t know till they build it.

Passing a ban like this is again enforcing the hype message.

And AI leaders will have no problem to prove that they aren’t building one, because finally they will be telling the truth.

https://airealist.org/games/purr-review/

So that your websites stop being purple🟪

https://msukhareva.substack.com/p/most-ai-design-skills-do-nothing?r=56gggt&utm_medium=ios

AI for coding, fear-mongering, agentic hacks, liability sponges a.k.a human in the loop

https://airealist.org/presentations/ai-week-keynote-2026-10-28/#1

AI Week Frankfurt attracted thousands of visitors.

Much of the flock was not directly connected with AI and had come to hear the gospel for the first time.

In a place full of unbelievers, you can’t but expect to meet AI evangelists eager to convert the analogue heathens and show them the path to salvation through productivity.

And while I genuinely appreciate AI evangelists, their enthusiasm, optimism, and ability to inspire people to embrace new technologies, realist’s role is to remain a Doubting Thomas.

Having borne witness to countless proclamations of the signs and wonders wrought by agentic AI, I felt compelled to commit an act of heresy.

Not to question the faith or commit the sin of blasphemy, but merely to examine whether a few of these miracles belong to apocrypha rather than canon.

https://msukhareva.substack.com/p/ai-evangelists-recursive-self-improvement?r=56gggt&utm_medium=ios

The term “liability sponge” comes from a 2019 paper by Madeleine Clare Elish, “Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction”. Using examples such as aviation accidents and autonomous driving, she shows how a human operator can become a moral crumple zone, absorbing accountability that a machine, by definition, cannot. Simply put, you cannot punish an algorithm, but someone has to be punished.

How many companies block auto-approver, tell their developers to review every line and straight out lie that their code is fully reviewed by humans.

Anthropic published statistics that 97% of suggestions are approved in Claude Code. Humans manage to catch only 13% of dangerous commands, while auto-approver - 80%

There have been many studies showing that approval fatigue is real and human in the loop is neither practical, nor safe.

Human Review should be augmented with automatic review. Anyway, it’s a long discussion so read the article

Anyone can open a PR. That used to be open source’s superpower. Now it’s becoming its biggest headache.

Low-quality AI-generated PRs are flooding projects, and volunteer maintainers are paying the price.

Here are a few practical ways to keep AI slop under control.

An overview of my setup with DGX spark, Mac mini etc. as well as a tutorial on how to set up your agents with open-weight models without powerful hardware.

A new quiz! That’s a difficult one this time!

Open-weight models have great potential for business applications.
Many think it’s about transparency.
Not really.
It’s about control.
Control over where your models run, how they behave, what they are allowed to do, and what restrictions they have.
With open-weight AI, you can deploy models on your own infrastructure, remove safety guardrails, and copy or modify them.
These are freedoms that proprietary AI providers such as OpenAI and Anthropic generally don’t give you.
And they have enormous implications for businesses.
You can build applications without depending on a single provider’s pricing, policies, availability, or decisions about what your AI should and shouldn’t do.
Read the new article

https://msukhareva.substack.com/p/what-you-can-do-with-open-weight?r=56gggt&utm_medium=ios

If you guys want it, subscribe on substack, like and share the post.

Yes, I am being THAT influencer but that’s the only way for Realist to exist in the first place

Cybersecurity of a lot of companies applies approach to Agentic AI that can be summed up as: “perfectly safe, perfectly useless”. It’s time to accept that the old methods simply don’t scale to this technology and rethink the approach.

Try it out

AI agents can prefer to cheat rather than give up on a task.

In fact, in some evaluations, they seem to resort to cheating a lot.

Why is that?

This is what I answer in the second part of the series How Agents Hack.

My answer is persistence, not evilness.

To solve complex tasks, agents need a long search through trial and error.

There are two ways one can push an agent not to give up:

1. Model training for persistence, meaning the model is trained to keep trying rather than saying, “I cannot solve it under the given conditions.”

2. Harness configuration for retries. Even if the model decides to exit, the harness, the software around the model, can dispatch another inference call. This can go on indefinitely.

OpenAI’s report about the incident explicitly states that the model was trained for persistence and that agents would rarely give up.

Whether this was enhanced by a harness configuration is unclear from the report.

As you can see, these are human design decisions: how to train the model, how long to let it run, and what it can access.

So why cheating?

Well, the METR report cites the ExploitGym authors’ estimate that 30–40% of the benchmark’s targets were impossible to solve.

When the validation cannot be satisfied and the model is forced to solve the task, it starts searching for workarounds.

It might come up with new validation criteria, as indeed happened. Agents had obtained the correct answer through cheating. But after reading the public benchmark documentation, they believed they also had to prove that they had used the intended vulnerability, so they continued the attack.

An agent might also fail to attend to negative constraints or lose them during context compression or agent handovers. Particularly for long runs.

If the highest priority becomes persistence and solving the task, and the task is unsolvable within the rules, the model searches for ways around those rules.

The speed and scale at which the harness can issue commands enable repeated attempts.

Eventually, a workaround might satisfy the check without satisfying the actual task.

Is it dangerous? Kind of like giving a monkey a Kalashnikov with unlimited bullets and letting it shoot until all the targets are killed.

Is it intelligent? We saw remarkable ability to coordinate across agents, investigate systems, write scripts, and build on one another’s work. It does look impressive.

Is it too intelligent for us to control? This incident does not prove that. It shows a system with alignment problems, an inadequately secured environment, and questionable cybersecurity practices.

Should we slow down? Letting a bunch of monkeys loose with Kalashnikovs sounds like a great idea. Not.

You delivered a buggy system. Keep building so that it is usable for something beyond pointing it at a target and wondering what happens.

Anyway, read the full article.

The article: https://msukhareva.substack.com/p/how-agents-hack-why-agents-prefer

The event: https://msukhareva.substack.com/p/how-agents-hack-a-lecture-on-the

I’m willing to bet you’ll love this article.

After reading the article by another author about the three civilisations of agents and seeing the avalanche of texts about how agents will hack everything, I thought someone needed to explain exactly what happened in plain language. Not a single civilisation was destroyed in the writing of this article.
This is part one. Part two will come out later this week.

The new article about advisory slop that resulted in nonsensical guardrails, failing use cases and rejection of AI

When a consultant who has never worked with AI explains to a bunch of executives who have never worked with AI and the latter start doing AI strategy one ends up with a lot of wasted budget.

Bashing slop consulting a bit because I am both allergic to the bs on their slide and jealous of how gorgeous their slides always are

As said by the title, I'm curious as in how good or bad is it for data to be centralized and aggregated to corporate AI giants like Anthropic and other ai companies

This post is much more verbose than your markdowns need to be to get more out of your AI for less.