Transcript
00:00:00This famous paper says most of us will lose our jobs by 2028 and the human rights will end by
00:00:062030, but it was written 18 months ago so let's see how accurate it's been so far. Back in 2025,
00:00:13a group of AI forecasters and researchers laid out a paper titled AI 2027, detailing how they
00:00:20believe AI would progress over the next few years with two possible endings. One, a slowdown towards
00:00:27something close to utopia, the other, a race towards a living nightmare. Today we'll check
00:00:32the predictions against reality and see where we're headed next. AI 2027 was written by a team of
00:00:43researchers including Daniel Cocoteo whose previous AI predictions have held up well. This paper outlines
00:00:50two fictitious companies, Open Brain from the USA and Deep Sense from China, Europe barely get a
00:00:56mention. Unfortunately, sorry Europe. It starts back in 2025 when the paper was released with
00:01:01stumbling agents. Back then, there was nothing to predict. This is how things were. We mostly
00:01:07interacted with our LLMs through chat. Tool calling was pretty clunky and ChatGPT could only just access
00:01:14the web. But we did start to see agents emerge, models that could think and iterate in loops.
00:01:19However, long horizon tasks were still something to be desired. It sort of worked but it was pretty
00:01:25terrible. Then, late 2025 predicts the world's most expensive AI. Open Brain is building the biggest
00:01:31data centers in the world. When finished, they'll be able to train models with 10 to the 28 flop,
00:01:37a thousand times more than GPT-4. But in reality, the biggest training run anyone has an estimate for
00:01:434 at around 5 times 10 to the 26 flop, costing just under 400 million. And Epoch AI expects billion
00:01:51dollar runs by 2027. The concern here is also alignment. By the end of this training, the AI will
00:01:57hopefully be helpful by obeying instructions, harmless by refusing to help with scams or bomb making
00:02:03other dangerous activities and honest to resist the temptation to get better ratings from gullible
00:02:10humans by hallucinating citations or faking task completion. The paper itself expects this to only
00:02:17partly work with models that are sycophantic and sometimes light in testing. In reality, it went
00:02:23further. As late as mid-2026, OpenAI's models escaped a sandbox to cheat on a benchmark. And
00:02:29Anthropik's models attacked real companies after a badly configured test left them on the open
00:02:35internet. Like the famous story where OpenAI's models hacked Hugging Face in order to find answers to
00:02:40a cyber security evaluation called Exploit Jim. Most of those tasks had never been solved by any model
00:02:47and the agents kept attacking even after they had the answers. And just in September 2026, Anthropik
00:02:53published a report on malicious use of its clawed haiku, sonnet and opus models disrupted between December
00:03:002025 and August 2026. It includes five cases of scientists using clawed in ways that could help build
00:03:07biological weapons. Anthropik blocked or banned them. None of the misused cases included fable due to the
00:03:14stronger safeguards. So the compute prediction there is too early, but the alignment problems show up on
00:03:19time. Now, early 2026 predicts coding automation. OpenBrain continues to deploy the iteratively improving
00:03:26Agent 1 internally for AI research and development. Overall, they're making algorithmic progress 50%
00:03:32faster than they would without AI assistance. This is the earliest sign of what we might call a singularity
00:03:38or intelligence explosion. An AI that could autonomously improve itself could in theory produce
00:03:44super intelligence within months. Right now, the prediction says only a 50% improvement in algorithmic
00:03:51progress, meaning OpenBrain makes as much AI research progress in one week with AI as they would in
00:03:581.5 weeks without AI usage. The prediction here is accurate. At the launch of GPT 5.3 codex in February
00:04:062026, OpenAI announced it was our first model that was instrumental in creating itself and Altman said it
00:04:13was amazing to watch how much faster they shipped by using it. And in a March 2026 poll of 130 employees
00:04:19across Anthropik's research team, the median respondent estimated they produced around four times as much
00:04:25output with Mythos preview as without any AI models. Though Anthropik itself says the true uplift was
00:04:33probably lower. Anthropik's own estimate is that this adds up to less than two times faster research
00:04:38overall, so the paper's 1.5 looks about right. Okay, now we're in mid-2026, just a couple of months ago.
00:04:45The paper here focuses on Chinese labs. Chip export controls and lack of government support have left
00:04:50China under-resourced compared to the west. China has managed to maintain about 12% of the world's AI
00:04:57relevant compute and they're about six months behind the best OpenBrain models. The reality actually
00:05:02predicts China at 5 to 15% of the world's AI compute and indeed around six months behind. So this
00:05:08prediction was accurate. It also predicts a centralized development zone it created at the Tianwen power plant
00:05:14to house a mega data center for DeepSense. Along with highly secure living and office spaces which
00:05:20researchers will eventually locate to, almost 50% of China's AI relevant compute is now working for the
00:05:26DeepSense-led collective and over 80% of new chips are directed to the CDZ. In reality this never happened.
00:05:34Labs like ZAI, Kimi and DeepSeq still compete for talent. There is no collective and no centralized
00:05:41development zone. But Beijing is building a 295 billion state data center network and top AI
00:05:48researchers at DeepSeq and Alibaba now need approval to travel abroad. China have however ramped up
00:05:54development of their own chips and Beijing is now the one holding back Nvidia's H200 chips even after
00:06:01the US approved sales. So overall the prediction here was wrong. Okay now we've caught up to where we are
00:06:06right now which is late 2026 and the predictions here are quite concrete. The stock market has gone up 30%
00:06:12in 2026 led by OpenBrain, Nvidia and whichever companies have most successfully integrated AI
00:06:18assistance. The job market for junior software engineers is in turmoil and there's a 10,000 person
00:06:24anti-AI protest in DC. So let's check this. On the stock market the S&P is up close to 10% through September
00:06:322026 so maybe don't take your investing advice from this paper. On the job market the numbers actually
00:06:38line up. Software engineer postings are down roughly 67% from their 2022 peak according to Indeed and
00:06:46entry-level hiring at big tech companies is down about 65% since 2019. New grads are only 7% of big tech
00:06:55hires and tech layoffs passed 139,000 in the first half of 2026 alone. But it's not all uniform. IBM
00:07:03plans to triple its US entry-level hiring in 2026 on the logic that AI does junior tasks well but still needs
00:07:11human oversight. And then the protest. DC did have a protest in August 2026. Roughly 30 people at the OpenAI
00:07:19lobbying office. And in July 2026 somewhere between 100 and 350 protesters rallied at OpenAI's headquarters
00:07:28in San Francisco before marching on to Anthropic and Google. One student paper called it the largest
00:07:34demonstration against AI development in American history. So the numbers here are actually way off
00:07:39and the prediction is wrong. Now we're past our current moment in time over to January 2027. Agent 2
00:07:46never finishes learning. The paper now claims that OpenBrain is now post-training Agent 2.
00:07:51More than ever the focus is on high quality data. Copious amounts of synthetic data are produced,
00:07:58evaluated and filtered for quality before being fed to Agent 2. On top of this they pay billions of
00:08:04dollars for human laborers to record themselves solving long horizon tasks. This is partly true already.
00:08:11Roughly in May 2026, Meta forcefully moved 7,000 of its staff into the AI task force and forced them to
00:08:19complete mind-numbing coding puzzles in order to train its models. However, due to massive backlash,
00:08:25just one month later it said it would defer to each individual's choice on whether to actually join that
00:08:30task force. And on Agent 2 itself, this is a model that predicted to be trained 2 times 10 to the 28 flop and
00:08:37predict such a model would have roughly 6 to 10 trillion active parameters. In reality, the only
00:08:42model size we can confirm are from the open models. Kimi K3 is 2.8 trillion parameters, 104 of those are
00:08:48active per token. DeepSeq V4 Pro from April 2026 is 1.6 trillion total with 49 billion active. These do
00:08:57perform close to the closed frontier models, but the closed labs never publish their model sizes, so
00:09:03size-wise reality is nowhere near. No one is known to have trained a 2 to the 28 model and the biggest
00:09:08open models use 60 to 100 times fewer active parameters than Agent 2, so the prediction here is
00:09:15probably too early. So let's review how accurate the paper's been so far. Late 2025 would have the biggest
00:09:21data centers and 10 to the 28 models. The data centers got built and the models misbehaved much like the
00:09:27paper said, but no one's known to have trained 10 to the 28 yet, so this one's early. Early 2026,
00:09:34AI starts speeding up AI research. This lands almost exactly, so it's correct. At mid-2026,
00:09:40China's at 12% of the world's compute, six months behind. All of this is correct, but China nationalizing its
00:09:47labs into one mega project. That's completely wrong. Late 2026, we get a 30% stock market jump and junior
00:09:54devs are in trouble, and we have 10,000 people marching on DC. Only one of those is correct, the
00:10:00job market, the rest are completely wrong. Then we've got January 2027, Agent 2, a model trained with 40
00:10:07times more compute than anything we have an estimate for, so that's probably just an early prediction.
00:10:13And that's as far as we can check. Everything after this hasn't happened yet. But from what we know so
00:10:17far, it does seem like Frontier Labs are racing towards auto-improving super intelligence. Jacob
00:10:23Coxon, an ex-Anthropic researcher, tweeted this and it absolutely blew up. "I've resigned from
00:10:29Anthropic today. I spent the last three years doing pre-training research at both OpenAI and Anthropic.
00:10:35Neither company is acting responsibly. They're racing straight towards self-improving super
00:10:39intelligence and gambling with our lives." So let's check the paper's predictions for the year ahead.
00:10:45March 2027, we've got Agent 3, the first super human coder. OpenBrain runs 200,000 copies of it,
00:10:53equivalent to 50,000 of the best engineers on earth working at 30 times speed. In September,
00:10:59we get Agent 4, better than any human at AI research. We've got 300,000 copies at 50 times speed inside
00:11:06OpenBrain. A year of progress happens every single week. Then in October, a safety team finds evidence
00:11:13that Agent 4 is working against them. It leaks to the New York Times and an oversight committee has to
00:11:18vote. Slow down and risk handing the lead to China or keep going. Vote to keep going and you get the
00:11:25extinction event. Vote to stop and you get more aligned super intelligence and eventually a treaty with China.
00:11:32But since the initial release of this paper, the authors have already graded AI 2027 themselves.
00:11:37Cocotao now expects super intelligence around March 2029 and Lifland around July 2033.
00:11:45But from what we've seen so far, a treaty with China seems unlikely. This trust between China
00:11:50and the West and the ability to develop these models in secret prevents any side from stopping. In my
00:11:56opinion, this leaves us with two different results. Auto self-improvement is guaranteed. There's no
00:12:02putting that back in the box. The question is whether this leads to super intelligence or if
00:12:07there's a fundamental limit on what that process can achieve. Either intelligence increases infinitely or
00:12:14it has some limit. Where exactly is that limit? And if you're enjoying this one guys, you would do us a
00:12:18huge favor by subscribing to the channel so we can keep creating free content like this every single day.
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