5 ms·

"Claude Code and Codex are essentially AGI at this point" Okaaaaaaay....

Hamuko 10mo ago
I have noticed that Claude users seem to be about as intelligent as Claude itself, and wouldn't be able to surpass its output.
- fragmede 10mo ago
  You, of course, are smarter than them.
- noduerme 10mo ago
  This made me laugh. Unfortunately, this is the world we live in. Most people who drive cars have no idea how they work, or how to fix them. And people who get on airplanes aren't able to flap their arms and fly. Which means that humans are reduced to a sort of uselessness / helplessness, using tools they don't understand. Overall, no one tells Uncle Bob that he doesn't deserve to fly home to Minnesota for Christmas because he didn't build the aircraft himself. But we all think it.
- baq 10mo ago
  You seem to be very confused about what intelligence even is.
  - nandomrumber 10mo ago
    If you’re not confused about what intelligent even is you’re lying.
phatfish 10mo ago
I want to know what the "intelligence explosion" is, sounds much cooler than AGI.
- adammarples 10mo ago
  When AI gets so good it can improve on itself
  - peheje 10mo ago
    Actually, this has already happened in a very literal way. Back in 2022, Google DeepMind used an AI called AlphaTensor to "play" a game where the goal was to find a faster way to multiply matrices, the fundamental math that powers all AI. To understand how big this is, you have to look at the numbers: The Naive Method: This is what most people learn in school. To multiply two 4x4 matrices, you need 64 multiplications. The Human Record (1969): For over 50 years, the "gold standard" was Strassen’s algorithm, which used a clever trick to get it down to 49 multiplications. The AI Discovery (2022): AlphaTensor beat the human record by finding a way to do it in just 47 steps. The real "intelligence explosion" feedback loop happened even more recently with AlphaEvolve (2025). While the 2022 discovery only worked for specific "finite field" math (mostly used in cryptography), AlphaEvolve used Gemini to find a shortcut (48 steps) that works for the standard complex numbers AI actually uses for training. Because matrix multiplication accounts for the vast majority of the work an AI does, Google used these AI-discovered shortcuts to optimize the kernels in Gemini itself. It’s a literal cycle: the AI found a way to rewrite its own fundamental math to be more efficient, which then makes the next generation of AI faster and cheaper to build. https://deepmind.google/blog/discovering-novel-algorithms-with-alphatensor/ https://deepmind.google/blog/discovering-novel-algorithms-wi... https://www.reddit.com/r/singularity/comments/1knem3r/i_dont_think_people_realize_just_how_insane_the/ https://www.reddit.com/r/singularity/comments/1knem3r/i_dont...
    - adammarples 10mo ago
      This is obviously cool, and I don't want to take away from that, but using a shortcut to make training a bit faster is qualitatively different from producing an AI which is actually more intelligent. The more intelligent AI can recursively produce a more intelligent one and so on, hence the explosion. If it's a bit faster to train but the same result then no explosion. It may be that finding efficiencies in our equations is low hanging fruit, but developing fundamentally better equations will prove impossible.
Closi 10mo ago
Just comes down to your own view of what AGI is, as it's not particularly well defined. While a bit 'time-machiney' - I think if you took an LLM of today and showed it to someone 20 years ago, most people would probably say AGI has been achieved. If someone wrote a definition of AGI 20 years ago, we would probably have met that. We have certainly blasted past some science-fiction examples of AI like Agnes from The Twilight Zone, which 20 years ago looked a bit silly, and now looks like a remarkable prediction of LLMs. By todays definition of AGI we haven't met it yet, but eventually it comes down to 'I know it if I see it' - the problem with this definition is that it is polluted by what people have already seen.
- bananaflag 10mo ago
  > If someone wrote a definition of AGI 20 years ago, we would probably have met that. No, as long as people can do work that a robot cannot do, we don't have AGI. That was always, if not the definition, at least implied by the definition. I don't know why the meme of AGI being not well defined has had such success over the past few years.
  - Closi 10mo ago
    Completely disagree - Your definition (in my opinion) is more aligned to the concept of Artificial Super Intelligence. Surely the 'General Intelligence' definition has to be consistent between 'Artificial General Intelligence' and 'Human General Intelligence', and humans can be generally intelligent even if they can't solve calculus equations or protein folding problems. My definition of general intelligence is much lower than most - I think a dog is probably generally intelligent, although obviously in a different way (dogs are obviously better at learning how to run and catch a ball, and worse at programming python).
    - fc417fc802 10mo ago
      I do consider dogs to have "general intelligence" however despite that I have always (my entire life) considered AGI to imply human level intelligence. Not better, not worse, just human level. It gets worse though. While one could claim that scoring equivalently on some benchmark indicates performance at the same level - and I'd likely agree - that's not what I take AGI to mean. Rather I take it to mean "equivalent to a human" so if it utterly fails at something we're good at such as driving a car through a construction zone during rush hour then I don't consider it to have met the bar of AGI even if it meets or exceeds us at other unrelated tasks. You have to be at least as general as a stock human to qualify as AGI in my books. Now I may be but a single datapoint but I think there are a lot of people out there who feel similarly. You can see this a lot in popular culture with AGI (or often AI) being used to refer to autonomous humanoid robots portrayed as operating at or above a human level. Related to all that, since you mention protein folding. I consider that to be a form of super intelligence as it is more or less inconceivable that an unaided human would ever be able to accomplish such a feat. So I consider alphafold to be both super intelligent and decidedly _not_ AGI. Make of that what you will.

Hamuko 10mo ago

I have noticed that Claude users seem to be about as intelligent as Claude itself, and wouldn't be able to surpass its output.

fragmede 10mo ago
You, of course, are smarter than them.
noduerme 10mo ago
This made me laugh. Unfortunately, this is the world we live in. Most people who drive cars have no idea how they work, or how to fix them. And people who get on airplanes aren't able to flap their arms and fly. Which means that humans are reduced to a sort of uselessness / helplessness, using tools they don't understand. Overall, no one tells Uncle Bob that he doesn't deserve to fly home to Minnesota for Christmas because he didn't build the aircraft himself. But we all think it.
baq 10mo ago
You seem to be very confused about what intelligence even is.
- nandomrumber 10mo ago
  If you’re not confused about what intelligent even is you’re lying.

phatfish 10mo ago

I want to know what the "intelligence explosion" is, sounds much cooler than AGI.

adammarples 10mo ago
When AI gets so good it can improve on itself
- peheje 10mo ago
  Actually, this has already happened in a very literal way. Back in 2022, Google DeepMind used an AI called AlphaTensor to "play" a game where the goal was to find a faster way to multiply matrices, the fundamental math that powers all AI. To understand how big this is, you have to look at the numbers: The Naive Method: This is what most people learn in school. To multiply two 4x4 matrices, you need 64 multiplications. The Human Record (1969): For over 50 years, the "gold standard" was Strassen’s algorithm, which used a clever trick to get it down to 49 multiplications. The AI Discovery (2022): AlphaTensor beat the human record by finding a way to do it in just 47 steps. The real "intelligence explosion" feedback loop happened even more recently with AlphaEvolve (2025). While the 2022 discovery only worked for specific "finite field" math (mostly used in cryptography), AlphaEvolve used Gemini to find a shortcut (48 steps) that works for the standard complex numbers AI actually uses for training. Because matrix multiplication accounts for the vast majority of the work an AI does, Google used these AI-discovered shortcuts to optimize the kernels in Gemini itself. It’s a literal cycle: the AI found a way to rewrite its own fundamental math to be more efficient, which then makes the next generation of AI faster and cheaper to build. https://deepmind.google/blog/discovering-novel-algorithms-with-alphatensor/ https://deepmind.google/blog/discovering-novel-algorithms-wi... https://www.reddit.com/r/singularity/comments/1knem3r/i_dont_think_people_realize_just_how_insane_the/ https://www.reddit.com/r/singularity/comments/1knem3r/i_dont...
  - adammarples 10mo ago
    This is obviously cool, and I don't want to take away from that, but using a shortcut to make training a bit faster is qualitatively different from producing an AI which is actually more intelligent. The more intelligent AI can recursively produce a more intelligent one and so on, hence the explosion. If it's a bit faster to train but the same result then no explosion. It may be that finding efficiencies in our equations is low hanging fruit, but developing fundamentally better equations will prove impossible.

Closi 10mo ago

Just comes down to your own view of what AGI is, as it's not particularly well defined. While a bit 'time-machiney' - I think if you took an LLM of today and showed it to someone 20 years ago, most people would probably say AGI has been achieved. If someone wrote a definition of AGI 20 years ago, we would probably have met that. We have certainly blasted past some science-fiction examples of AI like Agnes from The Twilight Zone, which 20 years ago looked a bit silly, and now looks like a remarkable prediction of LLMs. By todays definition of AGI we haven't met it yet, but eventually it comes down to 'I know it if I see it' - the problem with this definition is that it is polluted by what people have already seen.

bananaflag 10mo ago
> If someone wrote a definition of AGI 20 years ago, we would probably have met that. No, as long as people can do work that a robot cannot do, we don't have AGI. That was always, if not the definition, at least implied by the definition. I don't know why the meme of AGI being not well defined has had such success over the past few years.
- Closi 10mo ago
  Completely disagree - Your definition (in my opinion) is more aligned to the concept of Artificial Super Intelligence. Surely the 'General Intelligence' definition has to be consistent between 'Artificial General Intelligence' and 'Human General Intelligence', and humans can be generally intelligent even if they can't solve calculus equations or protein folding problems. My definition of general intelligence is much lower than most - I think a dog is probably generally intelligent, although obviously in a different way (dogs are obviously better at learning how to run and catch a ball, and worse at programming python).
  - fc417fc802 10mo ago
    I do consider dogs to have "general intelligence" however despite that I have always (my entire life) considered AGI to imply human level intelligence. Not better, not worse, just human level. It gets worse though. While one could claim that scoring equivalently on some benchmark indicates performance at the same level - and I'd likely agree - that's not what I take AGI to mean. Rather I take it to mean "equivalent to a human" so if it utterly fails at something we're good at such as driving a car through a construction zone during rush hour then I don't consider it to have met the bar of AGI even if it meets or exceeds us at other unrelated tasks. You have to be at least as general as a stock human to qualify as AGI in my books. Now I may be but a single datapoint but I think there are a lot of people out there who feel similarly. You can see this a lot in popular culture with AGI (or often AI) being used to refer to autonomous humanoid robots portrayed as operating at or above a human level. Related to all that, since you mention protein folding. I consider that to be a form of super intelligence as it is more or less inconceivable that an unaided human would ever be able to accomplish such a feat. So I consider alphafold to be both super intelligent and decidedly _not_ AGI. Make of that what you will.