
building @heyollieai // product + engineering in consumer AI // prev. co-founded Redemption Games (acq. @AppLovin)
California
Joined February 2010
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Chao Wu 伍超 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.
Chao Wu 伍超 retweeted
Excited to introduce @ReactorfieldAI Cohort 1:
@merge - brain-computer interfaces with no implants, $250M seed led by OpenAI and Bain
@episteme - a new R&D institution, cofounded by Louis Andre and Sam Altman
@RetroBio_ - unicorn longevity startup, adding 10 healthy years to human life. In phase 1 trials
@NorthwellHealth - NY's largest healthcare system, with Chief Biotech Officer @itchdoctor
@StrandTx - programmable mRNA medicines out of MIT, lead cancer drug already in patients
@MayoClinic - turning leading clinical science into new ventures represented by @vera_mucaj
@xiliotx - publicly traded biotech making cancer therapies to activate inside tumors
@marathonfusion - producing the world's most valuable isotopes from compact fusion reactors
@KopraBio - viruses that turn cancer cells into factories for their own destruction
@radifymetals - hydrogen-plasma reactors that turn rare-earth oxides directly into metal
@FoundationManf - rapid domestic CNC machining for frontier hardware
@MassMagnetics - US-made high-performance magnets from recycled rare earths
@algabiosciences - turning cattle methane into calories, peer-reviewed cuts of up to 63%
@lumehealth - building a wearable that measures cortisol continuously from sweat
@liu_liu_lemon - smart materials for med tech and robotics, @Harvard PhD, Fulbright Fellow
AI transformed coding, science is next
@ReactorfieldAI makes scientists and deep tech startups AI-native
Built by me and @berkbuilds to accelerate science with frontier AI
Unfortunately, I agree with a lot of this.
However, I don't think the need for an assistant is limited to Type A strivers. Life comes at you in waves: a new baby, a relocation, buying a house, caring for aging parents. There are moments when almost anyone desperately wants things taken off their plate. That's why I think picking the right wedge matters so much.
There's also an interesting value-capture problem here. A great assistant might create enormous value precisely by giving you more time to spend elsewhere. How much of that value can the assistant itself capture?
So it is still early days, and I wouldn't write off the entire category just because consumers don't want to make decisions. Market often emerges when friction falls far enough.
The problem with all of these AI personal assistant things is that people don’t actually want to do stuff.
You fucking get that right? Do you understand that?
Just like AI tools in the workforce, AI assistants don’t actually abstract decisions for you, they let you accelerate decision making.
So instead of making one decision a day and then lamenting you can’t do more, AI assistants are going to let you make ten decisions a day and act on all of them.
A miracle?
Nah dog, a fucking nightmare. Nobody except type A strivers who need Twitter fodder actually want that.
Let me tell you a story.
When I got back from college I was inspired. If I could take on an elite university with grit and determination, so could everyone I know.
I was all razzed up to get people I know to achieve their potential. What I learned after many years of trying is that absolutely nobody wants to realize their potential.
People want to hang out with friends and complain. That’s how the world has worked for ten thousand years. This weather fucks. Can you believe the cows walked off.
People don’t want enablement. People don’t want you to take all of their excuses.
TV is mindless passive activity. AI assistants are not. They’re telling people that they can achieve more. And people absolutely do not want that.
Let me tell you another story.
Once upon a time I lead a big process overhaul at work and it was great. Two years later I hired a contractor to do something similar. But I personally didn’t want to have to do a bunch of work.
Within a week I knew I fucked in. This contractor kept coming to me with decisions I needed to make. Dude I wanted to not have to think about this.
Again, a helper when you don’t actually want help is a disaster.
None of these AI assistants will take off because people don’t actually want assistants.
Chao Wu 伍超 retweeted
get the building blocks right and the next capability ships faster than the last. none of this was built for the benchmark, but it's validating to see the work hold up
My whole feed has been Muse and Instinct for the past few weeks.
Meanwhile, we at @heyollieai are tied with Muse on online tasks in the new assistant benchmark.
And we're the only one of the three that even got scored on group chats because that's where parents are. Nobody's coordinating soccer carpool in a single-player chat thread.
X users might chase the hype, average parents just want Ollie to work every day.
Thanks @DavidPawlan for benchmarking us and publishing the results. 🫡
Great day meeting so many people in product who I’ve followed and learned from for years.
Thanks @lennysan and the team for putting on such an amazing event. Looking forward to the next one!
Just landed at SFO, only took me 15mins to overhear someone saying ARR on the phone. Good to be back 🌁
Wonder what @Blender’s download spike looks like over the past few days 👀
GPT-6 Astra makes the singularity feel closer than ever.
So I asked it to simulate crossing an event horizon.
Try it yourself: universelab.org/descent
Chao Wu 伍超 retweeted
The family-focused AI assistant wants access to the details of your everyday life, but says it won’t use that data to train AI models or share it with others.
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