Marco Abis retweeted
This is one of the most important pieces of social science on human and AI interaction that Iāve seen in some time. Experts follow agentic advice the vast majority of the time (causally), but only a tiny fraction credit the agentāmajority think they came to decision on their own volition.
Reminds me of the classic Nisbett and Wilson study on people following outside influence without realizing it
ā But as more work is handed to AIāincluding AI safety researchāthese judgment calls are the main levers for people's own values to influence outcomes.
If people defer to AI without noticing, then "human in the loop" might provide less assurance than people expect.ā
Marco Abis retweeted
What if Anthropic just...stopped? Unilaterally? Right now?
I talked to @DKokotajlo @_NathanCalvin @geoffreyirving about why it's time to stop framing AI as a prisoner's dilemma and just do the right thing.
I hope a lot of people inside and outside Anthropic read this
š§µ 1/n
Marco Abis retweeted
I think it's only moderately embarrassing for Zitron that he got this stuff wildly wrong. It's valuable to have someone making the bear case! But it's extremely frustrating that he hasn't admitted this was completely incorrect and explained where he thinks he went wrong.
Some entertaining highlights from wheresyoured.at/wheres-the-m⦠20 months ago:
- "Anthropic has projected (made up) that it will make at least $12 billion in revenue in 2027"
- "This company is not worth $60 billion. Anthropic has raised $14.7 billion to create an also-ran Large Language Model company"
- "Anthropic, much like OpenAI, has categorically failed to productize its Large Language Model"
- "Its CEO is also a sleazy conman who, like Sam Altman, continually promises that his company's AI systems will become powerful and autonomous in a way that they have never shown any possibility of becoming."
- "Any investor in Anthropic needs to seriously consider what it is they're investing in. Its products are both unpopular and commoditized, and it lost $5.6 billion last year! Stop dancing around this fact! Stop it!"
- "thereās a lot of demand to put AI in stuff and some demand to buy stuff with AI on it by enterprises buying software, but little evidence to suggest significant user adoption or usage, Iād argue because Large Language Models do not lend themselves to features that provide meaningful business returns."
- "When a company uses the term āagent,ā they are intentionally trying to be deceitful, because the term āagentā means āautonomous AI that does stuff without you touching it.ā The problem with this definition is that everybody has used it to refer to āa chatbot that can do some things while connected to a database,ā which is otherwise known as a chatbot."
- "It isnāt working. The users arenāt there. The revenue isnāt there. The best time to stop this was two years ago, and the next best time is as soon as humanly possible. I have said that generative AI is a group delusion in the past, and I repeat that claim today."
- "we are approaching the limit of what the transformer-based architecture can do, if we havenāt already reached it. No amount of beating off about test-time compute and connecting Large Language Models to other Large Language Models is going to create a new use case for this technology"
- "generative AI is OpenAI. The consumer adoption of this software has completely failed, and appears to be going nowhere fast. ChatGPT is sustained entirely on deranged, specious hype drummed up by a media industry"
... and my favorite ...
- "I will keep writing this stuff until Iām proven wrong."
Marco Abis retweeted
We are publishing 4 new benchmark reviews: 1 is Verified, and 3 are Flawed. 4 more benchmarks lacked sufficient information to assign a verdict.
Our main bottleneck to reviewing more benchmarks is reviewer capacity. Info on applying to join our team is in the thread!
Marco Abis retweeted
I don't think "everything is fine," but anyone claiming that php2shell is "the worst day in cybersecurity history" needs to explain why there's more risk here than:
Innumerable celebrity vulns and exploit PoCs,
Serious protocol bugs in TCP/IP/DNS,
Out-of-control worm incidents,
Nation-state attacks on economies/critical infrastructure e.g. Russia->Ukraine,
Kernel packet-parsing bugs in popular operating systems,
The proliferation of AI social engineering tools since 2022,
The proliferation of AI hacking tools.
If you can't explain why this time is different/worse, with reference to clear technical mechanisms and attacker motivations, you don't have a serious argument..
I also think this kind of alarmism detracts from the genuine fact that we have a looming crisis in security due to AI which we need correct risk models for
š± "DeepInfer is the high-performance inference framework that powers DeepSeek's large-scale production services.
This open-source release provides offline inference with the core features needed for serving at scale. "
github.com/deepseek-ai/DeepIā¦
weekend reading and reflecting
Marco Abis retweeted
To help make advanced models more accessible, Muse Spark Open is optimized to run on two Apple M5 Ultra Mac Studios with the latest MLX framework.
It also runs on leading inference engines like SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, llama.cpp, and Unsloth with optimizations for AMD, Apple, Intel, and NVIDIA silicon.
Developers can further customize the model with PyTorch, including TorchTitan for fine-tuning.
"Once agents let anyone change almost anything, 'who can' stops mattering and 'who should' takes over. The boundary is authority: do the work where you own the decision, not where you don't."
tessl.io/blog/merge-rate-meaā¦
Marco Abis retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Marco Abis retweeted
"Making your team happy is not the job [of a manager]. Someone who canāt handle disappointing their direct reports or making them upset will never be an effective leader." charity.wtf/p/if-your-team-i⦠< post full of truth bombs from @mipsytipsy. Business outcomes come first.
Marco Abis retweeted
Join us next Wednesday!
Marco Abis retweeted
A new paper shows that when AI translates a math proof into Lean, passing the Lean check says nothing about whether the original proof is right.
They show a chatbot turning a wrong proof into a valid Lean proof by silently fixing the error.
Knowing when a statement can be translated faithfully is provably harder than the Halting problem, so no AI translator can always do it.
Marco Abis retweeted
Papers We Love Milano is back! š
Join us on Oct 14th! Valeria Zuccoli presents
"Repetita Non Iuvant: Why Generative AI Models Cannot Feed Themselves."
Discover how self-generated data degrades LLM output+diversity
š Red Hat Italy, Via Fara 26 (7th Fl) ā° 18:30
Ticketsš
š¤ Made with AI
Marco Abis retweeted
Small bird, fast wings, Kolibri is here.
78B parameters. 3.46B active. Up to 1M tokens of context. Built in Europe.
Now the weights are yours. Run it on your own hardware, under Apache 2.0.
Building Ziraph, I kept scratching itches around LLM-assisted dev and Claude Code. So I'm extracting a few:
⢠a comms bus for agents to coordinate
⢠a meta-IDE with observability panels
⢠an SDLC policy enforcer
The last might be useful to others, so I'll polish it up.
If only I'd waited 48h before posting this.
My "on-device AI is a margin decision" post weighs 6 axes but assumes the cloud is a stable utility you rent.
The US suspending Fable 5 + Mythos 5 adds a 7th: availability / continuity / sovereignty.
ziraph.com/blog/on-device-aiā¦




























