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The godfather of AI is so terrified of his creation, he is now tidying up his affairs. He believes that we maybe, I guess by now, have four years left. He thinks there is a greater than 50% chance of AI taking over. So I actually think the risk is more than 50% of the existential threat.
Imagine NASA's chief planetary defense officer estimating a greater than 50% chance of an asteroid wiping out humanity in the next 4 years. But why? We have examples like Deepseek R1 teaching itself a novel reasoning technique entirely on its own. We're witnessing an AI have an original insight without any prompting or instruction. It's not copying humans. It's thinking independently. And that self-awareness is happening much faster than researchers predicted.
It is strikingly plausible that by 2027 models will be able to do the work of an AI researcher or engineer. That doesn't require believing in sci-fi. It just requires believing in straight lines on a graph. This video is based on the report by ex-OpenAI researcher Leopold Ashen Brener. According to Washington insiders, the report is circulating among senior officials. Find the full paper in the description.
Only 2 years ago, OpenAI released GPT4, and it shook the very foundation of what we believed AI was capable of. GPT4 was an AI system that could write code and essays, could reason through difficult math problems, and ace college exams. A few years ago, most thought these were impenetrable walls. A decade earlier, models could barely identify simple images of cats and dogs. Four years earlier, GPT2 could barely string together semi-plausible sentences. Now, we're rapidly reaching a point where we can't even design benchmarks difficult enough to challenge frontier AI models. Benchmarks used to take decades to crack and now often take just months. These models are acing everything we can throw at them.
There have been people who have seen this for far longer. They were scoffed at, but all they did was trust the trend lines. The trend lines are intense, and they were right. To appreciate just how far we've come in such a short time, consider this evolution. In 2019, GPT2 was the equivalent of a preschooler who could barely count to five. Just one year later, GPT3 could tell actual coherent stories and write simple code comparable to an elementary school student. By 2022, GPT4 had leaped to producing complex code and solving advanced high school math problems, outscoring most humans on standardized tests. We've witnessed AI progress go from preschooler to gifted high schooler in just 4 years.
The pace of advancement in AI compared to other fields is unprecedented in human history. The progress over the recent scale is quite amazing. Our our very first reasoning was like a top 1 millionth competitive programmer in the world. People thought that was very impressive. It's like wow, in AI it's you know, in the millionth best people that do this. That's pretty good. We then had a model that got to like top 10,000. Uh 03 is the 175th best program competitive programmer in the world. I think our internal benchmark is now around 50, and maybe we'll hit number one by the end of this year. So that's like an amazing rate of scale for more compute in this new paradigm, and we don't see any signs of that stopping.
The godfather of AI, Jeffrey Hinton, warns that every few years they say, "Hey, neural nets are overhyped, and it's all about to come crashing down and stop," and they've been wrong every time. Soon we won't be able to measure how competent an AI is by giving it tests. Dan Hendris also created humanity's last exam, the most challenging AI benchmark ever. It's composed of 3,000 questions that seem like complete gibberish to everyone but the most experienced domain experts. Once AI can solve problems that only the top in their field can comprehend, it becomes too difficult to design even harder tests. Just think about how crazy that is for a second. We're already shifting to measuring AI agents' ability to make money rather than test scores. OpenAI's latest benchmark already evaluates if Frontier LM can earn $1 million from real-world freelance software engineering tasks. Yes, AIs are already 40% of the way to becoming self-made millionaires.
Initially, Frontier AI models couldn't score over 10% on humanity's last exam. Two weeks later, OpenAI's Deep Research scored 26%. At the current rate of progress, experts predict that humanity's last exam will most likely be solved within the next 1 to 2 years. But how did this seemingly impossible acceleration happen? The magic of deep learning is that it just works. The trend lines have been astonishingly consistent despite naysayers at every turn. The next few years will bring another leap as dramatic as GPT2 to GPT4, potentially taking us from high school to PhD level intelligence across all domains.
Three main drivers of AI progress make this possible. Let's dive into the first: computing power. When looking at how OpenAI trains Sora, notice that at the base compute, you start seeing vague outlines of images, but scale it up to 32x, and they transform completely. Picture a single computer. Now imagine a warehouse filled with them. Then visualize thousands of warehouses worldwide, all dedicated to a single purpose, training increasingly smarter AI systems. Let's take a look at why this seemingly simple approach—throw more computers at the problem—has worked so well over the last decade. The most famous example is Moore's law. For decades, this law predicted that computer processing power would double roughly every two years. An increase of about 1 to one and a half per decade. We can count thems or orders of magnitude of improvement. A 3x improvement is 0.5. 10x is 1, and 100x is 2, and so on. It's hard to understate just how big of an impact this has had on society, but AI compute growth has completely blown Moore's law out of the water. We're now seeing improvements that are five times faster than Moore's law predicted. Yes, five times faster than Moore's law. But even this pales in comparison to what's coming. OpenAI and the US government have already announced plans for Project Stargate, a data center rollout plus a training run rumored to use 3M or 1,000 times more compute than GPT4, with an estimated budget exceeding one hundred billion. Yes, 1,000 times more than GPT4, all in the next few years. The growth is so extreme that we have to use logarithmic charts just to display it on a single graph. But when we convert this to a regular linear scale, the true magnitude becomes clear. It's quite literally off the charts.
While compute gets most of the headlines, algorithmic efficiency, the ability to do more with less, has been quietly revolutionizing AI. It's the second main driver of AI progress. Think of it like developing better learning techniques instead of just studying longer. It's just as important as computing power, but relatively underappreciated. Let's look at a concrete example. Going back to the math benchmark, in just 2 years, the cost to achieve 50% accuracy on this test plummeted by a factor of 1,000 or 3. What once required a massive data center can now be accomplished on your iPhone. Algorithmic progress in the moment often feels random. Think Newton stumbling upon the theory of gravity by watching an apple fall from a tree. But when you zoom out enough, you can see the long-run trend is remarkably consistent. You have slow growth, then rapid growth, then a leveling off as the particular paradigm matures. Over the past decade, researchers have found that we've consistently gained a half anom of compute efficiency per year. 1 of compute efficiency means that we can run a model at the same level of effectiveness for 10 times lower cost. To put this in perspective, what required 10 GPUs 2 years ago can now be done with just one. Leopold Ashen Brener's analysis shows that the algorithmic efficiency gains between GPT2 and GPT4 were massive, roughly 1 to 2 of effective compute gains. That means a 10 to 100 times cheaper model just because researchers discovered some better algorithms. If this trend continues, and there's no sign it's slowing down, by 2027, we'll be able to run a GPT4 level AI for 100 times cheaper. Imagine this: If cars had improved at AI's rate, a $50,000 Tesla would cost $500 and travel as fast as a rocket in just 4 years.
These perspective estimates aren't set in stone, though. As we find more breakthroughs, it becomes harder to find the next one. We might fall short of these estimates. However, it's also just as possible we might surpass them with breakthroughs that accelerate this process even faster. The transformer architecture was introduced in 2017 and delivered a t-fold improvement in efficiency, doubling the yearly usual gain in a single breakthrough.
Of course, every exponential growth story faces potential limitations. What used to be one of the most widely discussed concerns is what experts called the data wall, where we might run out of useful training data for the AI models. It was a compelling argument. After all, there's only so much high-quality human-generated content in the world that the models can learn from. But recent breakthroughs by Deep Seek's R1 in OpenAI's 03 proved we likely already found ways around this limitation. As Goran notes, much of the point of a model like 01 is not to deploy it, but to generate training data for the next model. Every problem that an 01 solves is now a training data point for 03. This creates a self-improving cycle where each generation trains the next, effectively demolishing the data wall through synthetic data generation. We're now witnessing AI systems that improve themselves faster than human engineers could improve them.
The third and most unpredictable driver of AI progress is what Leopold Ashen Brener calls unhoblings, where we remove limitations that hold AI systems back from using their incredible raw intelligence. For example, imagine you're trying to solve a complex math problem with one major restriction: You must blurt out the first answer that comes to mind. You can't use paper or show your work. That's how early AI models operated. To solve this, researchers started giving LLMs their own chain of thought, allowing AIs to break down problems step by step, dramatically improving their problem-solving abilities. All it took to solve an obvious hobbling was a small algorithmic tweak. Deepseek's R1 and OpenAI's 01 and 03 are a continuation of this. We were able to unhubble them by letting them think about a problem for even longer, for minutes instead of seconds. But perhaps the most striking example of an unhoubbling came with the leap from GPT 3.5 to ChatGPT. Researchers were able to go from a base model to a useful chatbot because of reinforcement learning from human feedback, or RLHF. An RLHF small model was equivalent to a non-RLHF 100 times larger model.
Right now, leading AI labs are racing to implement the next major unhobling technique, giving their models what researchers call scaffolding. Rather than just asking a model to solve a problem, have one model make a plan of attack, have another propose a bunch of possible solutions, have another critique it, and so on. It's similar to how a team of experts might tackle a complex project, and it massively improves performance on benchmarks. Without scaffolding, AIs won't make the leap from being chatbots to drop-in remote workers. Giving AIs access to tools can skyrocket their performance almost overnight. Imagine trying to multiply 463 by 78 in your head or drive somewhere without GPS. AIs without tools face similar limitations. OpenAI's Deep Research scored 26% on humanity's last exam when their model was allowed to browse the internet and use Python for coding. But OpenAI's 03 Mini scored 15% for comparison. Another critical limitation researchers are working on is the context length: the amount of information an AI can keep in its memory at once. When GPT3 was first released, it could only process about 2,000 tokens. One token is roughly equivalent to about one word. So approximately four pages of text. GPT4 expanded this to 32,000 tokens, or about 64 pages. Gemini 1.5 Pro shattered all expectations with a context window of 1 million tokens, easily 10 large books worth of text. Gemini 1.5 Pro was even able to learn a new language, a low-resource language not on the internet, from scratch, just by putting a dictionary and grammar reference materials in context. Imagine dropping a new employee into a software company. No matter how brilliant they are, they can't be effective without first understanding the codebase, reading documentation, and learning from their co-workers. AI faces the same challenge. And the difference between 2,000 tokens then and 1 million tokens now is like the difference between remembering only your last meeting versus recalling every conversation from your entire first month on the job.
Perhaps the most significant recent breakthrough in unhubbling comes from a fundamental shift in how we scale AI systems. Traditionally, we'd feed the model the entire internet and every book ever written, piling on as much data as possible. This approach, called pre-training, is like trying to create a genius by stuffing their brain with as much info as possible. But we've hit diminishing returns with pre-training. However, researchers have discovered the next leading paradigm: post-training improvements. They're continuing to teach existing AIs after the base model has already been trained. One of the biggest new post-training improvements is surprisingly simple: The ability to think longer before responding. When OpenAI tested their unreleased O3 model, they allowed it to spend 30 minutes of thinking time on the ARC AGI benchmark rather than forcing 03 to answer immediately. To put this in perspective, these models think roughly 50 times faster than humans. This is like giving someone 25 hours to carefully consider a single question on a test. A survey by Epoch AI found that these unhoblings made AI systems 5 to 30 times more powerful. For scale, these improvements rival the gains from both massive computing power increases and breakthrough algorithmic advances.
Today's most advanced AI models are still significantly hobbled. Consider what remains to be unlocked: They don't have long-term memory. Their ability to use tools is extremely limited. While OpenAI's operator allows basic computer use, it still gets confused and stuck all the time. They can mostly only engage in short back-and-forth dialogues. Rather than going away for a day or a week, thinking about a problem, researching different approaches, consulting other humans, and then writing you a longer report or pull request. They're mostly not personalized to you, just a generic chatbot with a short prompt, rather than having all the relevant background on your company and your work. If you're imagining GPT6 in 2027 as just a more intelligent version of today's chatbot, you're missing the bigger picture. We're not moving toward better chatbots. We're moving toward true AI agents that can function like skilled remote co-workers. But to achieve this transformation, researchers need to solve the onboarding problem. GPT4 has the raw intelligence to handle many professional tasks, but it doesn't have any relevant context. Hasn't read the company docs or Slack history or had conversations with members of the team. A smart new hire isn't that useful 5 minutes after arriving, but they are quite useful a month in. It seems like it should be possible, for example, via very long context to onboard models like we would a new human coworker.
Right now, ChatGPT is like a smart high schooler trapped in an isolated box that you can only text with. But with multimodal models like OpenAI's operator, you have AI systems that can interact with computers much like humans do. Future AI systems will have digital avatars to join video calls, conduct research, collaborate with colleagues, and use the same software tools as human workers. They'll be true digital colleagues who can independently handle complex projects from start to finish. This unhoubling process could create a sonic boom effect in AI adoption. Companies today must build extensive custom infrastructure to make AI systems useful in the workplace. But once these limitations are removed, implementing AI might become as simple as clicking the "add new remote team member" button. You can see here the distinction between what we currently have and what is projected in future models. The leap from chatbot to true AI agent is driven by even larger improvements across all three factors.
What Leopold Ashen Brener is projecting for 2027 isn't just another incremental improvement. It's a transformation as dramatic as the leap from GPT2 to GPT4. Think about that comparison for a moment. GPT2 could barely write coherent sentences, while GPT4 can ace advanced exams and write sophisticated code. This next leap could potentially launch AI past PhD level expertise across a wide variety of fields. The math is striking. We're expecting a total increase of 5MS in effective compute combined with major breakthroughs in removing AI's current limitations. That's a 100,000 times increase. To grasp the sheer magnitude of this improvement, if GPT4 required 3 months to train, a model with GPT4 level capabilities in 2027 could be trained in just 1 minute.
What makes this particularly significant is the possibility of AI beginning to improve itself. Once these systems can effectively perform AI research, something the trend lines suggest could happen by 2027, the pace of progress could become unfathomable. Imagine tens of thousands of AI researchers working around the clock, potentially compressing a decade of algorithmic progress into a single year. This isn't just about creating a more sophisticated version of ChatGPT. We're talking about systems that could automate all cognitive jobs. As with every generation before them, every new generation of models will dumbfound most onlookers. They'll be incredulous when very soon models solve incredibly difficult science problems that would take PhDs days; when they're whizzing around your computer doing your job; when they're writing code bases with millions of lines of code from scratch; when every year or two the economic value generated by these models 10xes. It won't be longer before they're smarter than us. Forget sci-fi, count thems. It's what we should expect. AGI is no longer a distant fantasy. Scaling up simple deep learning techniques has just worked. The models just want to learn, and we're about to do another 100,000x by the end of 2027.
Hey, I'm Drew. For a deeper dive into the timeline for achieving AGI, check out this video next. I'm a small channel, and this video took me a month to make, so your support really helps out. Thanks a lot.