
building in ai, web & neurotech! founding ai eng @propane_ai 🇩🇰 | prev: sr ai eng @neuronsinc 🇩🇰 comp neuro @imperialcollege 🇬🇧 cs @unibirmingham
LDN🇬🇧/KBH🇩🇰
Joined October 2021
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have people you admire and want to become more like?
match them on repetitions, it's a simple, beautiful trade of time
100% and this is why you need to experience the world, form a point of view, commit, take some risk
the alternative is a slow decent.. so you may as well enjoy the process, personally stamp something!
Ben Fleming 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.
Ben Fleming retweeted
This guy re-created all the adobe apps with opus-5.5, ported them to rust, and opensourced them
something really cool is happening
Readers added context they thought people might want to know
These projects are partial open-source Rust reimplementations of Adobe apps (built rapidly with AI agents), not complete recreations; their READMEs and roadmaps document many missing features and features still in development.
github.com/storytold/phot…
github.com/storytold/vect…
github.com/storytold/vect…
github.com/storytold/film…
github.com/storytold/film…
github.com/storytold/ligh…
getartcraft.com
one of the more serious issues in the uk
we can change it for the better!
Replying to @rcolvile
Heathrow:
- Heathrow’s third runway, at £21bn for just the runway, will be 10 to 70 times more expensive than similar projects elsewhere
- Its planning application alone, at £800m, costs more than actual new runways in Madrid, Dublin, Chicago, Brisbane, even Manchester...
basically compute you get per plan is going down, yes outcomes are greater as models are better/efficient
yet it is the same for open source - but:
- you own the compute
- no propriatary data leaves your company
- switch cost is tending to 0
how will this pan out?
Hi,
Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan.
Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago.
(a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want.
(b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions.
(c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent.
(d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet.
I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news.
Codexingly,
Tibo
ngl i think the gym i was just at was playing ai music
maybe to get around the licensing fees?
please world, let’s collectively not go down this path…
very important for the future of the uk
- uk jobs are some of the most exposed
- one of the highest energy prices in comparable nations - sluggish infra building
we have to get to the root cause of these problems and skate where the puck is going - great work @jujulemons
I am increasingly frustrated about the state of conversation about AI in the UK. It feels like we are in never ending cycles of deluded thinking. Under current trends, Britain will be defenceless against the transformative effects of AI and have no leverage over development at the frontier.
We are the most exposed economy to AI in the world. We have very little data centre capacity and few credible plans to expand – and our high energy costs mean we would struggle to even if we tried.
I am publishing a paper today on what the UK should do to build that leverage, let the economy capture value from AI and prepare the state to adapt, however AI develops.
The three strategies are as follows:
1. Build the infrastructure that gives us leverage. Fix the grid, fix planning, make Britain a place frontier AI can run.
2. Let the economy capture the value of AI. Reform the regulatory, labour and talent barriers that stop British firms adopting AI.
3. Build a state that can adapt to a changing world. Build a tax base that survives the labour market changing, public services that use AI to deliver better, and capacity to weather the upcoming geopolitical storms.
Two years ago, we published a similar report on AI - many recommendations of which ended up in the action plan. I remember feeling hopeful, but sadly the fundamental problems facing the country on AI have not shifted. I truly hope that this week of momentum culminates in action. There’s still a narrow path for the UK, but everyday that path is narrowing.
been out 'ere euromaxxing.. much needed breather in this crazy paced ai era
but very excited to be back to building!
can feel the hardware journeyman arc incoming
Ben Fleming retweeted
You can get 95% of the results you want by calmly putting one foot in front of the other. One former Navy SEAL friend texted me a principle used in their training: “Slow is smooth. Smooth is fast.”
in my rlmaxxing era..
absolutely fantastic resource on reinforcement learning from human feedback rlhfbook.com/
would highly recommend builds up from first principles!
kudos @natolambert huge asset for the community:)
is an interesting one
has occured to me a few times why any model provider would not prioritise use of their own paid tools etc.
for example as much as I love uv, after openai acquired it I'm sure both codex prioritises this for python projects - then the monetisation kicks in
📢 New issue brief: AI agents increasingly operate autonomously on your behalf — but nothing stops them from prioritizing their developers’ interests over yours. Our latest brief argues that agents should be classified as fiduciaries and required to act in users’ best interests. hai.stanford.edu/policy/desi…
on what builders should learn in 2026 - my 2¢:
to start:
- llm fundamentals - attention mechanism, pre/posttaining, inference
- software eng fundamentals - what excellent looks like, when to go fast vs slow
to skate where the puck is going:
- reinforcement learning and custom post training - harvey’s tenet, cursor’s composer
- evals




























