That includes anyone reading this message long after the lives of those reading it on its post date have ended.
Which of course raises the interesting question of how I can make good on this bet.
my position is I have no idea what is going to happen.
Even if you don't understand the technicals, surely you understand if any party was on the verge of AGI they wouldn't behave as these companies behave?
That's a bold claim, please cite your sources.
It's hard to find super precise sources on this for 2025, but epochAI has a pretty good summary for 2024. (with core estimates drawn from the Information and NYT
https://epoch.ai/data-insights/openai-compute-spend
The most relevant quote: "These reports indicate that OpenAI spent $3 billion on training compute, $1.8 billion on inference compute, and $1 billion on research compute amortized over “multiple years”. For the purpose of this visualization, we estimate that the amortization schedule for research compute was two years, for $2 billion in research compute expenses incurred in 2024."
Unless you think that this rough breakdown has completely changed, I find it implausible that Sora and workplace usecases constitute ~42% of total training and inference spend (and I think you could probably argue a fair bit of that training spend is still "research" of a sort, which makes your statement even more implausible).
"AI slop and workplace usecases" is a synecdoche for "anything that is not completing then deploying AGI".
The cost of Sora 2 is not the compute to do inference on videos, it's the ablations that feed human preference vs general world model performance for that architecture for example. It's the cost of rigorous safety and alignment post-training. It's the legal noise and risk that using IP in that manner causes.
And in that vein, the anti-signal is stuff like the product work that is verifying users to reduce content moderation.
These consumer usecases could be viewed as furthering the mission if they were more deeply targeted at collecting tons of human feedback, but these applications overwhelmingly are not architected to primarily serve that benefit. There's no training on API usage, there's barely any prompts for DPO except when they want to test a release for human preference, etc.
None of this noise and static has a place if you're serious about to hit AGI or even believe you can on any reasonable timeline. You're positing that you can turn grain of sand into thinking intelligent beings, ChatGPT erotica is not on the table.
If we continue the regime where OpenAI gets paid to buy GPUs and they fail, we'll have a funding winter regardless of AI's progress.
I think there is a strong bull case for consumer AI but it looks nothing like AGI, and we're increasingly pricing in AGI-like advancements.
If you're really serious about it put the money into a prediction market. Poly market has multiple AGI bets.
https://polymarket.com/event/openai-announces-it-has-achieve...
https://kalshi.com/markets/kxoaiagi/openai-achieves-agi/oaia...
By almost any definition available during the 90s GPT-5 Thinking/Pro would pretty much qualify. The idea that we are somehow not going to make any progress for the next century seems absurd. Do you have any actual justification for why you believe this? Every lab is saying they see a clear path to improving capabilities and theres been nothing shown by any research I'm aware of to justify doubting that.
LLMs are cool and fun and impressive (and can be dangerous), but they are not any form of AGI -- they satisfy the "artificial", and that's about it.
GPT by any definition of AGI is not AGI. You are ignoring the word "general" in AGI. GPT is extremely niche in what it does.
Definitions in the 90s basically required passing the Turing Test which was probably passed by GPT3.5. Current definitions are too broad but something like 'better than the average human at most tasks' seems to be basically passed by say GPT5, definitions like 'better than all humans at all tasks' or 'better than all humans at all economically useful tasks' are closer to Superintelligence.
> The Turing test, originally called the imitation game by Alan Turing in 1949, is a test of a machine's ability to exhibit intelligent behaviour equivalent to that of a human.
> The test was introduced by Turing in his 1950 paper "Computing Machinery and Intelligence" while working at the University of Manchester. It opens with the words: "I propose to consider the question, 'Can machines think?'"
> This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against the major objections to the proposition that "machines can think".
Ignoring the entire article including the "Strengths" section and only looking at "Weaknesses" is the only cherry-picking happening.
And if you read the Weaknesses section, you'll see very little of it is relevant to whether the Turing test demonstrates AGI. Only 1 of the 9 subsections is related to this. The other weaknesses listed include that intelligent entities may still fail the Turing test, that if the entity tested remains silent there is no way to evaluate it, and that making AI that imitates humans well may lower wages for humans.
Like I was watching Hinton explain LLMs to Jon Stewart and they were saying they came up with the algorithm in 1986 but then it didn't really work for the decades until now because the hardware wasn't up to it (https://youtu.be/jrK3PsD3APk?t=1899)
If things were 1000x faster you could semi randomly try all sorts of arrangements of neural nets to see which think better.
Why is growth over the last 3 years completely flat once you remove the proverbial AI pickaxes sellers?
What if all the slop generated by llms counterbalance any kind of productivity boost? 10x more bad code, 10x more spam emails, 10x more bots
Example: better than average human across many thinking tasks is done.
If you do not understand the core concepts very well, by any rational definition of "understand," then you will not succeed at competitions like IMO. A calculator alone won't help you with math at this level, any more than a scalpel by itself would help you succeed at brain surgery.
Ive actually hung around Olympiad level folks and unfortunately, their reach of intellect was limited in specific ways that didnt mean anything in regards to the real economy.
I agree with you, and I think that's where Polymarket or similar could be used to see if these people would put your money where their mouth is (my guess is that most won't).
But first we would need a precise definition of AGI. They may be able to come with a definition that makes the bet winnable for them.
jb1991 says >"Which of course raises the interesting question of how I can make good on this bet."<
Have children...
You're saying that we won't achieve AGI in ~80 years, or roughly 2100, equivalent to the time since the end WW2.
To quote Shane Legg from 2009:
"It looks like we’re heading towards 10^20 FLOPS before 2030, even if things slow down a bit from 2020 onwards. That’s just plain nuts. Let me try to explain just how nuts: 10^20 is about the number of neurons in all human brains combined. It is also about the estimated number of grains of sand on all the beaches in the world. That’s a truly insane number of calculations in 1 second."
Are humans really so incompetent that we can't replicate what nature produced through evolutionary optimization with more compute than in EVERY human brain?
Their potential upside is that OpenAI (a company with lifetime revenues of ~$10bn) have committed to a $300bn lease, if Oracle manages build a fleet of datacenters faster than any company in history.
If you’re not short, you definitely shouldn’t be long. They’re the only one of the big tech companies I could reasonably see going to $0 if the bubble pops.