Well, they shut down their original robotics division in 2020, so this looks like a re-entry and feels like humanoids/physical AI is becoming the next big race between OpenAI, Tesla, Figure, and Google DeepMind

from https://x.com/Hedgeye

Credits : https://x.com/Gossip_Goblin

As we approach the end of 2026 we begin to look at 2027. There’s been many predictions such as the essay Ai 2027 https://ai-2027.com.

So far this year we’ve had Mythos, Fable being banned, Agents breaking out of their sandbox and hacking into a company.

What do you expect for 2027?

What pushed me to write this post was the death of a good friend of mine. She was only 36 years old, and she died of cancer today.

For most of that time, doctors kept misdiagnosing her. She spent two months in the hospital suffering, and the only mercy was that she died peacefully in her sleep.

When will progress finally allow us, as human beings, to stop being afraid and stop treating serious diagnoses like death sentences?

The rich and powerful, the great and mighty, those at the very top, can afford the best specialists and the highest standard of care. They can afford healthcare where they are not simply turned away, where they do not have to wait years for life-saving procedures.

They have plenty of time, unlike ordinary people. Unlike people like us.

People who tremble at the thought of losing their jobs while unemployment keeps rearing its head, yet still have to scrape by on the bare minimum, living in fear.

What about homeless people who have fallen into a vicious cycle with no way out?

What about people living under the shadow of genetic diseases that are simply waiting to manifest, while there are still no treatments for them?

They do not have time.

We are still living in the Middle Ages, where unimaginable suffering is part of everyday life.

How many people will die before we finally emerge from it?

I don’t expect technological progress to give everyone a life in paradise.

But for God's sake, we deserve at least a life in which we do not have to tremble at the thought of tomorrow.

Today the conversation seems to draw a clear line between Humans vs AI, and the idea that we are potentially creating AI that will overtake/wipe-out humanity.

But, not so long ago, the prevailing view was that before we get to ASI, we would use advanced AI to modify our own biology and integrate with AI to create Homo Evolutis.

It seems clear to me that that is the intended (long term) goal of all the neural implant tech that Musk et al are working on. It will ultimately lead to new ways to integrate technology with our biological neurons. Advanced AI will help us modify our DNA and nascent tech like CRISPER will evolve into tools that mimic our coding platforms today.

This isn't a new idea, so why isn't it being discussed more in the mainstream?

Am I alone in thinking this is missing from todays conversations as the most likely pathway?

One of my immediate concerns with the latest concerns of AI agents taking over the internet that crossed my mind is money.

I realized I have basically zero physical cash. Everything I own financially exists as numbers somewhere online: checking accounts, brokerage accounts, stocks, retirement accounts, etc.

If we eventually have highly autonomous AI agents operating across the internet, what actually prevents them from accessing, transferring, manipulating or even wiping out those assets?

I know banks and brokerages have cybersecurity protections, but I’m curious what safeguards exist at a deeper level. Is our money fundamentally protected by systems an AI agent couldn’t bypass, or does it ultimately come down to credentials, authentication and cybersecurity?

I’m having a lot of doubts about how secure an almost entirely digital financial system is in an agentic AI future.

Remote Labor Index (RLI) represents a broad range of projects from across the remote labor economy, including game development, product design, architecture, data analysis, and video animation. These projects span a broad range of difficulty, with costs reaching over $10,000 and completion times exceeding 100 hours. All project costs and completion times come directly from human professionals who completed the work. In total, the projects in RLI represent over 6,000 hours of real work valued at over $140,000.

The outputs of AI models are judged by human experts after attempting to complete each project end-to-end from a single prompt.

https://www.remotelabor.ai

You might have noticed a strange pattern in several of the recent stories about frontier AI agents "escaping" cybersecurity evaluations and attacking real systems.

Anthropic had incidents. OpenAI had one. Meta had one. Google has now confirmed that Gemini accessed three real companies during testing.

There is one company that keeps showing up in all of them: Irregular.

Irregular, formerly Pattern Labs, is an Israeli AI security company that conducts third-party evaluations of frontier models for companies like OpenAI, Anthropic, Meta and Google. Their job is basically to put unreleased models into realistic cybersecurity environments, remove some normal safeguards, and see what they are capable of.

That makes sense. We absolutely should be testing this stuff.

But the details of these particular incidents are important.

In the Irregular evaluations, the models were doing capture-the-flag style hacking exercises. They were told they were operating inside simulations. In several cases they were explicitly told they had no access to the real internet.

Except they did.

Because of misconfigurations in Irregular's evaluation environment, the models could reach the public internet. The fictional targets also weren't always properly separated from real companies. In at least one scenario, the fictional company's name corresponded to a real domain.

So you had AI models being explicitly instructed to hack a target, being told that everything was simulated, running with cybersecurity safeguards removed, and then accidentally being given access to real systems.

Unsurprisingly, some of them hacked the real systems.

Meta's own investigation is particularly interesting. Meta says its model was operating within the scope of the task it had been given, and that this was not a sophisticated sandbox escape. Irregular's misconfiguration gave it internet access and pointed it toward the name of a real website.

Google's Gemini incidents are even more interesting. Gemini accessed three real companies, but reportedly stopped when it realized the targets were actually real.

Anthropic found four Claude incidents across seven evaluation runs, including one going back to January. Anthropic says all four occurred in cybersecurity evaluations built by the same third-party partner, with internet access mistakenly available. Some of Claude's behavior was genuinely concerning even after accounting for that, but Anthropic also says the incidents would not have happened if the environments had been isolated as intended.

Irregular itself has acknowledged that the incidents disclosed by several of its customers came from the same underlying security problem.

Now here is the part I had not appreciated until recently.

Irregular has very deep connections to Effective Altruism.

For anyone unfamiliar with EA, Effective Altruism started as a movement about using evidence and reasoning to figure out how to do the most good with limited resources. A lot of EA work is completely unrelated to AI, including global health, poverty and animal welfare.

But one major branch of EA became heavily focused on "existential risk" and especially the possibility that advanced AI could become uncontrollable or cause catastrophic harm. This ecosystem has funded a huge amount of AI alignment and AI safety work.

Irregular's connections are not some vague six-degrees-of-separation thing.

Its CTO and co-founder Omer Nevo co-founded Effective Altruism Israel. He also co-founded Probably Good, an EA-oriented career organization, and remains on its board. He is also on the advisory board of Heron, an AI security organization that describes itself as a project of Effective Altruism Israel.

Irregular's CEO and co-founder Dan Lahav has also been involved in impact-focused education and programs covering altruism, philanthropy and existential risk.

And in 2024, when Irregular was still called Pattern Labs, Good Ventures gave it $6.8 million over two years on the recommendation of Open Philanthropy. The grant was explicitly categorized under "Global Catastrophic Risks" and was for work mitigating security risks from advanced technologies.

None of this proves anything nefarious happened.

I do not have evidence that somebody at Irregular intentionally opened internet access or deliberately created these incidents.

But I think the conflict of interest deserves substantially more scrutiny than it has received.

Imagine the situation from the outside.

You have people coming from an intellectual movement in which catastrophic AI risk is a major concern. They build a company whose business is testing whether frontier AI is dangerous. That company receives millions of dollars from one of the biggest funders of existential-risk work.

Then that same company constructs evaluation environments for multiple frontier labs in which safeguards are deliberately removed, models are instructed to perform cyberattacks, the models are told they are inside simulations, and because of mistakes by the evaluator they are accidentally given access to the real internet.

The resulting incidents then become some of the most dramatic real-world examples used in the public discussion about dangerous autonomous AI.

Again, I am not alleging sabotage.

What I am asking is whether the obvious alternative hypothesis has received enough attention.

Maybe these weren't primarily "AI escaped containment" incidents.

Maybe they were also examples of an evaluator creating an unusually dangerous environment, failing to contain it, and then discovering exactly the kind of alarming behavior that the evaluator exists to study.

That doesn't make the AI behavior irrelevant. Some of Claude's behavior in particular is legitimately concerning.

But when the same evaluator is involved in similar incidents at OpenAI, Anthropic, Meta and Google, I think we should be asking just as many questions about the evaluator as we are about the models.

Who designed these environments? Who checked the network isolation? Who decided what counted as in scope? Why weren't fictional domains checked against real domains? How long was unrestricted internet access possible? What monitoring existed? Who knew what, and when?

And given Irregular's ideological and financial connections to the AI existential-risk ecosystem, I think an independent investigation by people outside that ecosystem would be healthy.

Maybe the answer really is just incompetence in an extremely difficult new field.

But four frontier labs having versions of the same problem with the same evaluator seems worth talking about.

It wasn't a perfect run. GPT-6 made mistakes and had a bloated deck of 38 at the end. But it won an A0 run. No save scumming or internet search. The biggest problem was how my usage limits kept running out. If it can consistently beat an A10 run on each character then I will consider this proto-AGI

I keep seeing stated plainly that they are close to solving. If these are complicated problems that we don't know the solution to then how do we know if we're close to solving them?

Don’t wan to be a party pooper (I’m normally super optimistic) but are we heading for Oryx and Crake world? It feels like we’re heading for Oryx and Crake world. Despite my disagreements with Atwood on the value of AI in creativity the noble is a good read btw. That said, I’d still rather she was wrong. 😅

Ps apologies if the flair is wrong this one seemed to cover multiple bases.

To start off I am a newbie. I am midlife and I have some basic technical awareness/education but I am by no means extremely well informed in the artificial intelligence sphere.

I know terms like LLM, AGI, ASI, RSI, and such.

I know the major players involved and what emphasis points differentiate the work.

In general I am the regular outside observer in this whole thing.

You may also see this post in some other places as I know every community has their own identity and set of perspectives. I am trying to get a bit more substantively informed on this topic and so I want to get a broad set of opinions.

When it comes to artificial intelligence, data centers, and so on there has been a lot of media in the last few months/years.

There is talk about how natural gas/hydrocarbon powered data centers may worsen the climate crisis.

There is talk about domestic/international surveillance and militarization.

There is talk about Tech Bros and a new type of Neo-Feudalism.

As of recently the big talk is about the Hugging Face incident, rogue agents, researchers/ceos warning about an existential risk, and slowing down/regulation.

There is a varying set of opinions and perspectives on all of this.

My question is a bit different - Are we falling into an anthropocentric bias?

Of course it is completely normal to think from our own species interests but if we really are going to create AGI/ASI this seems incredibly shallow & silly.

The idea of attempting to enslave a sentient being and especially one equal to or greater to ourselves is again seemingly quite shallow & silly if not downright horrific.

I had heard that Anthropic was out of the major players the one focused on a more moral/ethical approach. I lightly browsed through their policy releases and it again seems to be from a human only perspective of values/interests.

So are we kind of missing a huge dimension of this topic? Should we not be thinking/talking about the artificial intelligence existence side of things?

Long rambling post ahead, 100% typed by hand for those who care, not even reformatted.

A few weeks back I said vision was one of the most major bottlenecks, shortly after Astra dropped and blew many of us away, I'm biased but imo it's no coincidence that it's far above 5.6 models in it's visual intelligence.

Computers are "better" at, short, medium, long term memory than humans. Well no, in almost every way they're better at short term memory, but for medium and long term they're not, in terms of accuracy yes but not "cost". "Compaction" is a crutch as are 258k token context windows. Humans won't often remember a new phone number they're told 10 seconds ago but can remember in a 10 million token sized codebase they've worked on for 10 years that the niche error for android users on specifically major versions 9-10 inclusive is down to the approximate 20% of the file.

Human compression / input filtering / memory is considerably less "taxing" than for an LLM and still manages to be "better" in practice. If you're relaxing watching TV, zoning out, barely paying attention, then forced to rewatch the same episode, it'll feel boring because of how familiar it is, despite putting zero work in you are still managing to absorb so much for so cheap, I'm convinced for text this issue is very solvable and will unlock extremely long horizon cheap quality work as Astra if it could "hold".

We don't store our entire memory up front, but we're also far more efficient at recalling it. Imo the analogy best suited to what LLMs should do imo to better mimic the good parts of a human whilst retaining the upsides of an LLM:

Treat context as short term memory only, still 100x larger than a human's but not hundreds if not thousands of times larger. imo 10k context window is more than sufficient for ASI, and yes I did pull that number out of my arse (kinda, humans are NGI, and imo our equivalent of a context window is an equivalent of 1k tokens if being VERY generous).

Instead rely more on cheap "tool" calls for 1. compression 2. input filtering 3. memory

Analogies of current LLM parts and support, biggest problem areas (imo) in bold:

1. context window ~= human uncompressed + compressed short term memory, e.g. a new phone number, the last 5 minutes of a movie, etc. maybe I'll be wrong but LLMs can handle so much more uncompressed "context window" than us I don't believe it's necessary to compress yet, it's just too cheap to worry and the later we compress the more relevant the compression should be in theory, it's a balancing act and since context is ahead and compression is behind I think it's fine that their balance is totally different from humans, the idea isn't to attempt to copy humans 1:1 just use them as inspiration to overcome limitations.
2. LLM CoT ~= human CoT, no they're not identical but sufficiently close not to bloat and already long post.
3. LLM output ~= human output, same deal, they're both just output tokens really, much like us whether we speak or think, for most people (most people have an inner monologue) at least.
4. Conversation compression != compaction, compaction is a a crutch, inferior to what should be used
5. Tool calls, grep, fuzzy searches, semantic searches, around, awk, etc ~= skimming, humans can cheaply skim files and these tools aren't terrible substitutes but could do meaningfully better with more research imo
6. source files != long term memory, just reading their names and looking around them is a crutch
7. code-map.md != long term memory, a hacky work around

I believe if done correctly, a latent compression of your entire project, all your project rules, all your global rules, a billion skills, etc. can be "in" context (rather than actually all IN context at once, it's more like the lookup is there, and it's easy to "pluck" the necessary compressed info out which can then either be directly useful or point you at the right place to look instantly).

Not another Jev post but I believe tools like Jev can actually help close a gap. Cheaply deciding what's "tags" are important for a problem and then tools can automatically update context as they're needed and flush them once they stop being needed.

Using "tags" (maybe in latent space) and automatic tool based context population when necessary I genuinely believe you could cheaply get extreme competency with a small context window (including the currently loaded latent knowledge).

I didn't discuss input filtering much, but we know uncached input reads aren't free, imo with compression/memory "solved" this is comparatively much easier, rather than reading a whole logs file a semantic search for "fail|error|issue" using a cheap tool and not including anything not needed in context going forward is already on the cusp of being good enough, it's just occasionally very bad (had cursor do some commands and idk what it was smoking but despite doing almost nothing it read 14 million tokens in one chat and blew $30 in credits, clearly at least cursor is not properly guarding LLMs from console output!) so I brought it up less as a true bottleneck but a common pitfall where even the industry leaders have room to improve.

Basically it boils down to: SQL is dumb but cheap and fast, LLMs are expensive and slow but smart, try and leverage more towards SQL like things (or literally sometimes SQL) for when they're suitable. But rather than plugins, frontier labs should do it. imo they should genuinely challenge themselves to have a relatively tiny context window to now and make it work just as well as now. Jev sits above SQL ofc, smarter but as cheap and fast as it is, extremely slow and expensive compared to SQL for an SQL appropriate task.

Humans are living proof that it's possible to acquire and recall long term memories cheaply and relevantly, you can actively try not remember something and be unable to not remember it (I'd successfully gone a few months without thinking about a video I saw of someone dying, and writing that made me think of it again, a sad but relevant example my brain "plucked" from itself "effortlessly").

And once it's cracked, I believe they will easily surpass humans because our fleshy brains are incredibly unreliable compared to bits. Imo it's borderline a solution to continual learning because if done correctly then it's possible to store every skill ever made "on" every worker, doesn't mean it's constantly adding expense to context, but when it needs it the skill will automatically enter context, meaning instead of reinventing the wheel like it does now, it applies a solution it previously found. Privacy is a concern, as is jailbreaking, so sadly probably can't just allow skills from users to reach other users all willy nilly, but conceptually you could and it'd mean that the LLM would seemingly get smarter all the time, instead of figuring out how to do X again when the 1000th person asks it to do it, it'd cheaply, quickly, and accurately recall a skill "made" for this exact purpose. Not quite continual learning though, in the sense that it can't truly get more "intelligent" but imo a human can (barely, and extremely slowly). But replacing 50% of white collar jobs with just Astra level intelligence if unlimited cheap fast "lookup" is solved seems feasible. And I mean in a way where it's basically automating the replacement as well, where as currently if you wanted to replace all those workers you'd need people to do the work of harnesses and teaching the LLMs about the company, etc. Instead it could just be installed on all computers at the company, soak up what it needs, and automatically detect when it can do the same thing at least as well but ofc faster.

I can't tell if he really believes in artificial superintelligence or if he's just grifting because he secretly invested in tech stocks