James Vreeken 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.
they named it that because after 4 prompts all your tokens Argon
Replying to @Google
Gemini 4 Argon is our next era of frontier intelligence.
It shows significant improvements across benchmarks, setting a new state of the art for real-world long-horizon software engineering tasks.
With a name like Jev… hahaha this is all I hear every time I see stuff about Jev. @typesafeai I really like the model tho but that name...
James Vreeken retweeted
I might sound old, but I’m somehow reluctant to use open source libraries made with AI.
Before, someone had to be passionate enough to dedicate time to actually write the code, think about the API, developer experience, documentation, etc.
There was also something artisanal about a person knowing all the ins and outs about a codebase, knowing exactly why a certain line was written, and slowly sculpting the product.
Now you just write a few prompts plus another one for the AI-generated docs in your repo’s readme.
It feels like fast food and it has also lost its magic, because it seems like a new library is launched every day.
But probably, soon libraries will be a thing of the past anyways.
James Vreeken retweeted
Experimenting with a rare addition to my global CLAUDE.md:
"Always talk in ASD-STE100 Simplified Technical English. Always read CONTEXT.md files, and use their ubiquitous language."
Alright. I want you to read this.
Earlier today, I released a new component.
What you see is the finished result.
What you don’t see are the days of planning, research, testing, designing, simplifying, throwing ideas away, and trying again before I felt it was ready.
Within hours, it had been copied and ported to another framework by pointing an agent at my code.
Of course, it’s open source. They were allowed to do it.
And I’ve been around long enough, with multiple successful products, that I don’t care.
But what if this was someone’s first product?
What if they spent months building it, launched on Friday, and by Monday it had been cloned by agents, repackaged, and made free?
What happens when your second product is copied?
Your fifth?
Your tenth?
What happens when every new idea immediately becomes the next prompt?
What happens when your roadmap, your changelog, and your launch announcements become someone else input prompts?
What happens when models become good enough to copy bigger ideas?
What happens when “just execute better” stops being the answer?
What happens when “just build bigger” or “think wider” stops being enough?
What happens when both ideas and execution become cheap?
What happens when the creator pays the full cost of creativity but everyone else pays nothing to reproduce it?
What happens to creativity when AI makes copying effectively free?
What happens to the will to create when AI makes copying effectively free?
Look, copying is not new.
Copying at this speed, cost, and scale is.
People will tell you to think bigger.
But nobody starts "bigger".
Every one of us here started by making something small.
You keep going because, somewhere along the way, the work is rewarded.
Maybe people use it.
Maybe they pay for it.
Maybe they simply recognize the thought, effort, and care that went into it.
Copying once demanded effort.
You had to study the work and understand it to copy it.
That effort created appreciation for the person who made it.
Not anymore.
Creativity begins with small steps.
If we make those first steps feel pointless, we don’t just lose small products.
We lose the people who might have gone on to build the big ones.
We are uniquely positioned here.
We get to use and experience this life-changing technology before almost everyone else.
How we use it will set the default everywhere else.
That default will spread to writing, design, music, research, and every other field where someone still has to take the first creative risk.
We can use AI to build great things.
Or we can use it to strip every new idea for parts.
We decide.
Me: {Pretty much every prompt}
Claude: This is a meaty, multi-part feature.
...Ten minutes later... what would have taken weeks by hand... ~all tests are green and I've tested everything end to end using 67 agents blah blah blah its ready for you to review at your leisure.
James Vreeken retweeted
Your brain knows a bunch of English grammar rules that you were never actually taught.
Why does “little red ball” sound normal, but “red little ball” feels wrong? Or why is it “fun little red rubber ball” and not “rubber fun little red ball”? Most of us just instinctively know the right order — even though almost nobody was ever taught the actual rule.
It turns out there’s a pretty specific (and surprisingly strict) order we follow for adjectives, and we enforce it without even realizing it’s a rule. No wonder why they say English is one of the hardest languages.
Were you ever taught what order to put adjectives in? I don’t remember learning this.
James Vreeken retweeted
If you haven't seen yet, Timothy Ricks just published a video with his AI workflow...
AND IT'S USING SHIP STUDIO!!!
Incredible video, highly recommend watching it if you're finally ready to make the leap and start building with agents.
youtube.com/watch?v=Ae42g1lo…
James Vreeken retweeted
Absolutely brilliant.
This guy literally used AI to explain the whole Anthropic drama to his girlfriend using fruits.
James Vreeken retweeted
sooo what happens when we get some Fable-level open-source shit in the next 3-6 months?
GPU bans, moving goalposts, or some secret third thing? 🤔


























