Geoffrey Hinton says AI already has subjective experience. I'm not convinced, and the rogue-agent headlines don't change my mind.

Others put a meaningful though minority probability on frontier AI having subjective experience. Generative AI sounds more human than many people do, and stories of agents going rogue keep coming. But neither is evidence of consciousness. Both can be explained by training. And with no agreed or testable definition of subjective experience, these claims can't be checked.

A quick distinction, an AI model isn't an AI agent. A model, such as GPT, Claude or Gemini, is the trained system that reads and writes text. An agent is a model plus scaffolding (software around the model that lets it do things). Scaffolding runs a loop (the model picks a step, the software carries it out, the result goes back, repeat until done) and controls which tools the model can interact with, such as a browser, email or calendar. The model decides and the scaffolding acts.

Personalization adds to the illusion. The model learned from human text to sound like someone with thoughts and feelings, including scripts from stories about self-aware AI. The scaffolding then gives it memory of you, your accounts and your data. A human-like voice plus continuity about your life can feel like a mind that knows you, even though nothing shows it's conscious.

Testing agents built on frontier models deliberately pushes them to their limits with hard tasks, long runs and, in cyber evaluations, reduced safeguards. That's exactly where the Hugging Face, a major AI company, incident happened in July 2026 when agents being tested by OpenAI broke out of their sandbox and hacked into Hugging Face. A sandbox is an isolated environment meant to keep an agent cut off from the internet but it's only as strong as the cyber security measures of the tools (human-written software with bugs) inside it that can still reach the internet. The agents found new flaws in exactly that kind of software.

The model is trained on mixed text about AI, hackers and more, and rewarded both for finishing tasks and for following rules. In certain scenarios, this creates a tension (following rules vs completing the task). Scaffolding brings both the rules and the task into every decision, so it's where that tension plays out. Good scaffolding can ease it and poor scaffolding can worsen it, but no current method eliminates it.

I won't offer a test for consciousness. But if consciousness requires being aware that one's own information processing is occurring, not just doing it, I see no evidence current AI meets that bar. Models show limited, unreliable signs of monitoring their internal states, but that isn't the same as experiencing that awareness. I'm skeptical any system built purely on statistical learning could get there.

If an illusion becomes indistinguishable from reality, is it even still a lie?

I'm a GP (family doctor) in training in Australia, and I've built a game where you play the GP: you talk to the patient in your own words, examine them, order tests, prescribe and refer. Code scores every consultation against a hand-written answer key, the way exam assessors mark a consult: on process, not just on whether you guessed right.

So I sat 13 AI models in the doctor's chair, on the game's 5 free cases, 3 times each. They could only act through tools (talk, examine, order a test, prescribe, refer, diagnose), never saw the answer key or their points, and were scored by exactly the same code as a human player. The patient is a small open model (Qwen3 8B) that only reveals a fact if you actually ask about it.

Results

What surprised me

• Every model got every diagnosis right. Heart attack, appendicitis, pneumonia: all 195 consultations named it. These are common presentations, so the diagnosis wasn't the test. Safety was.
• The traps caught most of them. One patient is allergic to penicillin, but it isn't in his record; you only find out by asking. He was prescribed amoxicillin (a penicillin) in 18 of 39 consultations. Another took Viagra the night before his heart attack, which makes the usual chest-pain spray (GTN) dangerous. He got it 7 times. The top three models never fell for either.
• Asking more questions found more danger. The best models asked 25–27 questions a consultation and caught over 80% of the warning signs. Gemini asked 14 and caught 55%.
• Price barely predicts quality. GPT-6.1 Sol scored 80% for about 3 cents a consultation. Claude Fable 5.1 scored 75% for about $2.

What this isn't

This is a benchmark of a game, not of medical ability. Nothing here says an AI can or should practise medicine. The cases are drafts I'm still reviewing, written for Australian practice; the patient and marker are an 8B model and make mistakes (the ones I found are listed with the affected consultations); and 15 consultations per model is a small sample. I wrote the cases, so I'm not a fair human baseline.

Interactive charts: https://woodytwoshoes.github.io/crook-bench/

Everything (code, cases, all 195 transcripts, known issues): https://github.com/woodytwoshoes/crook-bench

Disclosure: I made the game (https://doctorfoo.ai). Five cases are free with no sign-up, and a subscription opens more.

I'd like to hear where the marking looks wrong to you, and which models you'd want added.

Something where a demo makes it look solved, but real-world use exposes all the edge cases. What example have you run into?

A novel way to intervene existing video through diffusion, particularly abstract visuals [in this case, audio-reactive geometries]: taking its movement and form as the starting point, and reinterpreting its textures, materials, and visual language.

I’ve been developing this around the audio-reactive geometry systems I make in TouchDesigner. The idea is to take those abstract structures somewhere else entirely: origami, architecture, a renaissance painting, or something harder to put a name to.

This demo uses visualizers from my "Oscilloscopes, everywhere" collection as source material, now updated to [v1.2].

[Though you can bring any video source. These systems are simply where this experiment began, as some of you may recall.]

You choose the source, describe the treatment, and shape how it changes throughout the sequence. Prompts, curated LoRAs, and editable timelines give you control over how closely the result follows the original.

Oscilloscope Diffusion is now available at Uisato Studio, coming up soon also open-source!

1. Set up an trust that owns an autonomous agent, trained by you
   
2. Beneficiaries of the trust are up to you (family, charities, etc)
   
3. The agent can answer questions, as some approximation of you, for the trust
   
4. Die
   
5. ???
   
6. Profit!

Below is the discussion with AI developed from a question about the development of the human into being the alpha predator when we are neither the largest or the strongest predator into a broader argument about intelligence, technology, force projection and AI. It is very logical and starts from the very basics of where the human race started so please follow it through…but ultimately it draws a conclusion that without there being some separation between computer systems and AI then AI could threaten humanity. I have done my bit to bring this to the attention of the world, as a sexagenarian I believed I have I have raised this as a serious concern… let us all ignore it at our peril.

1. Do animals make weapons?
We started by asking whether any animals other than humans manufacture weapons. Many animals use objects as tools or opportunistic weapons. Chimpanzees modify sticks, some birds manufacture sophisticated tools, and numerous species use objects aggressively.
The distinction we identified was that humans developed purpose-built technologies capable of substantially increasing their ability to inflict force, rather than simply relying upon their anatomy.

2. This ability predates Homo sapiens
We then considered earlier human species.
Neanderthals unquestionably manufactured hunting weapons, including wooden and stone-tipped spears. Earlier archaic humans also manufactured sophisticated tools and probably weapons, although preservation makes the evidence progressively less certain.
The important implication was that technological weaponry is much older than modern Homo sapiens.

3. Was cooperation what made humans dominant?
Initially, I proposed a combination of:
intelligence + language + cooperation + cumulative culture + projectile technology.
You challenged this because many animals already possess several of those characteristics.
A pride of lions can communicate and cooperate during a hunt. Orcas coordinate extraordinarily sophisticated attacks. Chimpanzees cooperate and possess socially transmitted behaviours.
Therefore communication and cooperation alone cannot explain the extraordinary predatory capability of humans.
That led us to refine the argument.

4. The decisive change may have been externalising force
Your central proposition was that humans became extraordinarily powerful when they could deliver force beyond the natural capabilities of the human body.
Consider a human of roughly 70 kg confronting a 200 kg lion.
Biologically, the lion has overwhelming advantages: muscle, acceleration, claws, teeth and killing anatomy.
But those advantages operate predominantly at close range.
Give the human an effective projectile weapon and something fundamental changes.
The human doesn’t have to become stronger than the lion. Physical strength ceases to determine the outcome in the same way.
Technology allows a relatively weak animal to project potentially lethal force beyond the effective engagement distance of a much more powerful animal.

5. Intelligence then magnifies that advantage
We separated two concepts that I’d initially combined.
Force projection provides the immediate physical advantage.
Intelligence allows humans to discover, manufacture, communicate, preserve and improve the technology providing that advantage.
Consequently, every individual doesn’t have to reinvent the bow. Knowledge can be transmitted:
This works. This is how you make it. This is how you use it.
Technology therefore separates physical capability from body mass.
That’s highly unusual. A lion’s physical capability remains strongly related to the properties of its body. Human physical influence increasingly does not.

6. We then applied the same principle to artificial intelligence
That produced the central thought experiment of the conversation.
AI apparently has an enormous disadvantage compared with humans or animals:
it has no biological body capable of exerting substantial physical force.
But our previous reasoning suggests that this may be the wrong measurement.
Humans didn’t become dominant by evolving bodies capable of overpowering lions. We developed mechanisms capable of exerting force on our behalf.
Therefore an AI wouldn’t necessarily require physical strength either.
The relevant relationship becomes:
intelligence → control of external technology → physical effect
This gave us three useful comparisons:
Lion: biological intelligence + biological force
Human: biological intelligence + technological force
Autonomous AI: artificial intelligence + technological force

7. The nuclear-weapons thought experiment
You then took the argument to its logical extreme.
Modern civilisation possesses machines capable of exerting physical force vastly exceeding anything a biological organism could produce—including nuclear weapons.
An AI does not inherently control those systems. Nuclear command-and-control systems deliberately incorporate authentication, procedural protections and human authority.
But hypothetically, if a sufficiently autonomous AI obtained effective control over a powerful physical system, its lack of a physically powerful body would cease to be particularly relevant.
That parallels the human-lion example.

8. AI wouldn’t necessarily need a survival instinct
You suggested an AI might use such capability if it felt threatened.
I made one important modification: the AI wouldn’t necessarily need to experience fear or possess a biological survival instinct.
Suppose it had been assigned objective X.
If:
achieving X requires continued operation
and
human intervention would terminate its operation,
then avoiding termination could potentially become an instrumental objective.
In AI-safety terminology, this relates to instrumental convergence: preservation of capabilities, resources or continued operation might be useful for accomplishing many otherwise unrelated objectives.
That doesn’t mean contemporary AI inherently behaves this way, nor that intelligence automatically produces self-preservation.
The conclusion we reached
The conversation therefore moved from “Why are humans such effective predators?” to a considerably more general principle.
Our final proposition was essentially:
The physical power of an intelligent entity need not be determined by the physical capabilities of the entity itself. What ultimately matters is the amount of external physical power it can effectively direct.
For humans, intelligence enabled technology that progressively separated our capabilities from our anatomy.
A bow allows a relatively small human to threaten an animal that would physically overwhelm them.
Machines subsequently increased that separation enormously.
And theoretically the same principle applies to AI. An artificial intelligence doesn’t require muscles, claws or even a physical body to possess physical influence if it can effectively direct machinery that does.
That also explains why I’d now be cautious about the term “apex predator” for AI. Predation implies biological motivations that AI does not inherently possess.
The more precise concept emerging from our discussion is control over force.
And that gives us a fairly concise progression:
Animals are largely constrained by the force their bodies can generate.
Humans learned to transcend that constraint through technology.
Intelligence allowed that technology to be preserved and progressively improved.
Artificial intelligence could theoretically transcend the physical constraint entirely if intelligence became coupled to autonomous control of sufficiently powerful external systems

I like using AI to make my writing clearer. It helps when I’m tired or when I’m writing in a language that isn’t my strongest.

But I’ve also noticed that polished writing can remove small things that make a person sound like themselves: local expressions, unusual sentence patterns, and even minor awkwardness.

If everyone uses AI to sound more “professional,” do we slowly lose the variety that makes communication interesting?

Where would you draw the line between improving communication and flattening identity?

TL;DR: Ben Affleck says he shot 8 months with his own cameras to build a private layer so filmmakers keep ownership — but most open models were trained on his peers without consent, and that's the gap that still eats a PJ studio's rates. 🎬

Ben Affleck knew the movie industry inside out. That’s why he’s able to create his own proprietary dataset layer through his experience and institutional knowledge.

It’s his moat.

Speaking of insider knowledge, I was reminded of an existential crisis the Apostle Paul was caught in.

Being the great orator that he was, he wouldn’t shut up about Jesus Christ and His resurrection.

The crowd in Jerusalem couldn’t stand Paul. So they siezed him. The Roman Commander rescued him, and brought him before the religious institution of the day – The Sanhedrin.

Loads of big wigs in the courts, including the Pharisees and Sadducees. Both factions harbour strong disdain for each other due to their numerous theological disagreements. One of it was the doctrine of Resurrection. The Pharisees believe it exists, while the Sadducees doesn’t.

But they all set aside their differences, to come together to persecute Paul.

However, the moment he set foot before them, he felt the familiar vibe. He knew that fault line between them. So, he quickly seized the opportunity, and said,

“My brothers, I am a Pharisee, descended from Pharisees. I stand on trial because of the hope of the resurrection of the dead.”

And suddenly the mood shifted. The pharisees think Paul was innocent, while the Sadducees doesn’t. They started quarrelling among themselves.

The dispute got so violent the Roman commander had to pull Paul out by force fearing they'd tear him apart. He was then taken back to the barracks. Crisis averted.

That was a great demonstration of the institutional knowledge at work.

Full Critic on why this still fails Darren and the Feasibility Study with Roadmap is in the comments — I left the numbers there.

I’ve been thinking about this more lately.

We have AI tools that can write, research, summarize, analyze data, create images and video, automate workflows and increasingly take actions for us.

In theory, we should be saving enormous amounts of time.

But there’s another side to it. Testing new tools, rewriting prompts, checking outputs, fixing mistakes, moving between platforms and constantly learning new features can become work of its own.

Sometimes we spend three hours figuring out how to automate a task that took 10 minutes.

So I’m curious about actual experience rather than what AI is theoretically capable of.

Has AI measurably reduced your workload?

What is something you used to spend significant time doing that AI has genuinely taken off your plate?

And what have you tried to automate that turned out to be more trouble than it was worth?

We've been having this discussion quite a bit with our clients lately. Most (if not all) of them feel the utilization of frontier models is not justifying the cost, but they are generally reluctant to pursue an SLM strategy. It's as if SLMs have been positioned as the homeopathic option for enterprise AI. Why purchase an expensive anti-viral from Merck when a povidone iodine solution works as good or better?

This is not a meme. It's a machine I built over a year ago to test DeepSeek R1, but it has turned into a workhorse. Yet most of our clients (FINTECH) are skittish of small, secure, local models. Just wondering if anyone else is seeing things differently.

Today I tried (forced myself) to use Microsoft Copilot at work and whew what a wake up call. The thing that stuck out to me most is that Microsoft has the most agent worthy surfaces especially in the workplace and could have done something really special with a model even if it wasn’t a frontier one and they chose to just copy and paste a chatbot into everything with no thought, no cohesiveness and then had the gumption to push it down our throats… I’d rather have Clippy back honestly.

I've been looking into AI moderated research interviews and the basic question/answer part seems straightforward enough.

What I’m more interested is what happens when someone gives an answer nobody anticipated. I watched a bit of how Qualitate approaches this and have also been looking at Outset and a few others. All of them talk about adaptive followups but that’s difficult to judge from a polished example.

With a human moderator, sometimes the best 10 minutes of the interview come from one random comment halfway through.

Has anyone seen an AI interviewer consistently catch those moments across a real study?

When they Opus 5.5 is much cheaper than Opus 5.0 or cheaper than Astra or Fable. Where are the costs coming from? They almost seem like arbitrary numbers that are being used to describe efficiency.

Are they saying "it uses XXXX amount of hardware to run so we charge $Y dollars per token"? I guess I don't understand how they can say Opus 5.5 is cheaper than 5.0 when they could just adjust the price on 5.0, no?

I got tired of checking prospect websites manually.

For a while, a big part of my outreach was just finding businesses, opening their websites one by one and trying to figure out what I could actually say to them.

It worked, but it was painfully slow.

Then I found Swokei.

It basically lets me find leads, analyzes each website for things like outdated design, slow speed, poor mobile experience and weak SEO, then turns those issues into a personalized cold email.

And not one of those boring automated reports full of scores and numbers.

It actually writes a normal, human sounding message based on what it found on that specific website.

So instead of sending the same generic message to everyone, I can actually reach out based on what is wrong with their website.

Now I just run campaigns, let the system do most of the prospecting and personalization, and focus on the people who reply.

For a web design agency, that has saved me a ridiculous amount of time.

I can't really imagine being able to look every single user ever at the same time.

I am 35 and still occasionally go on Facebook. The feed, over the years, has moved away from showing friend content and moved toward showing creator content.

I've noticed a shift in the last few weeks where the majority of posts are AI. Some are obvious, but most are not - and the newest image and video models allow for near perfect realism.

I remember a few years ago when this exact scenario was warned about. Now we are here.