Artificial General Intelligence may be just 2-3 years away. Here’s why experts are converging on 2027 as the critical year.
The question “when will AGI happen?” used to be met with vague answers spanning decades or even centuries. Not anymore. In a sobering conversation on Tom Bilyeu’s Impact Theory podcast, Dr. Roman Yampolskiy—a pioneering AI safety researcher—reveals why 2027 is emerging as the most likely timeline for achieving Artificial General Intelligence (AGI).
And if he’s right, the implications are staggering.
What Is AGI and Why Does It Matter?
Before diving into timelines, it’s crucial to understand what we’re actually talking about. Artificial General Intelligence (AGI) refers to AI systems that can match or exceed human cognitive abilities across virtually all domains—not just narrow tasks like playing chess or generating text.
“If you asked someone maybe 20 years ago and told them about the systems we have today, they would probably think we have full AGI,” Dr. Yampolskiy explains on the podcast. Current AI like GPT-4 is remarkably capable across hundreds of domains, but it still has limitations. “We probably don’t have complete generality,” he notes.
The current gap? Systems like ChatGPT still lack:
- Permanent, cumulative memory
- True lifelong learning capabilities after initial training
- Consistent novel contribution abilities in advanced domains
But Dr. Yampolskiy warns: “We’re getting closer and closer to where those gaps are closed.”
Why Prediction Markets Point to 2027
When Tom Bilyeu asks for a specific timeline, Dr. Yampolskiy doesn’t hide behind uncertainty: “It’s hard to predict. The best tool we got for predicting future of technology is prediction markets. And they saying maybe 2027 is when we get to AGI, artificial general intelligence.”
Prediction markets aggregate the forecasts of thousands of informed participants who put real money behind their predictions. These markets have proven remarkably accurate at forecasting near-term technological developments because they:
- Aggregate diverse expert opinions
- Penalize overconfidence with financial losses
- Update continuously as new information emerges
- Filter out pure speculation through monetary stakes
According to Dr. Yampolskiy’s analysis of these markets, AGI arriving around 2027 represents a consensus forecast—not an outlier prediction.
The Rapid Acceleration Nobody Predicted
What makes the 2027 timeline particularly striking is how dramatically it has accelerated. Just a few years ago, even AI researchers expected AGI to arrive decades from now. What changed?
The Scaling Hypothesis Keeps Working
“If I just give another I don’t know trillion dollars worth of compute to train on and more data will I get to AGI?” Dr. Yampolskiy poses. “A lot of graphs, a lot of patterns suggest yeah it’s going to keep scaling. We’re not hitting diminishing returns.”
The “scaling hypothesis”—the idea that simply making AI models bigger with more computing power and data leads to better performance—continues to hold true. And critically, there’s no sign it’s stopping.
AI Is Already Making Novel Scientific Contributions
Perhaps most significantly, AI has crossed a critical threshold: it’s now making genuine contributions to cutting-edge research.
“Seeing on social media scientists from physics, economics, mathematics, pretty much all the interesting domains post something like ‘I used this latest tool and it solved a problem I was working on for a long time.’ That’s mind-blowing,” Dr. Yampolskiy observes.
“It’s no longer operating at the level of middle schooler or even high schooler. We’re talking about full professor level.”
This represents a fundamental shift. When AI can advance the frontiers of human knowledge, we’re approaching AGI territory.
The Self-Improvement Cycle Is Beginning
One of the most significant developments is AI starting to improve AI itself. Dr. Yampolskiy describes how systems are already being used to:
- Design new AI model architectures
- Optimize training parameters
- Create novel algorithms
- Even design the computer chips they run on
“There is definitely an improvement cycle,” he states. While humans are still in the loop, “long term, I think all the steps can be automated.”
Once that automation is complete, we enter a phase of recursive self-improvement where each generation of AI creates a more capable next generation—potentially at exponentially increasing speeds.
Why 2027 Isn’t Science Fiction—It’s a Conservative Estimate
Some might dismiss 2027 as hype, but Dr. Yampolskiy makes a compelling case that it’s actually plausible, perhaps even conservative.
Current Systems Are ~50% There
“So I think we’re getting close to full-blown AGI. Maybe we are at like 50%,” Dr. Yampolskiy assesses current systems. If we’re already halfway there, reaching 100% in 2-3 years isn’t far-fetched.
The Historical Trend of Underestimation
Interestingly, AI researchers have consistently underestimated progress. Dr. Yampolskiy notes: “I think JAN [Yann LeCun] is known for making certain predictions about what models are capable of. And then within a week, people demonstrate that no, in fact, they can actually do that.”
Even prominent AI scientists have repeatedly claimed certain capabilities were impossible—only to be proven wrong within months.
The Investment Wave
The sheer scale of investment in AI development suggests industry insiders believe short timelines are realistic. We’re seeing billions of dollars flowing into AGI development, which indicates confidence from those with the most information.
“Based on the amount of investment we see in this industry it seems like people are willing to bet their money that scaling will continue,” Dr. Yampolskiy observes.
From AGI to Superintelligence: The Rapid Leap
Here’s where the timeline gets truly concerning. Dr. Yampolskiy doesn’t just predict AGI in 2027—he expects superintelligence to follow almost immediately after.
“I think soon after super intelligence follows. The moment you automate science engineering, you get this self-improvement cycle in AI systems. The next generation of AI being created by current generation of AIS. And so they get more capable and they get more capable at making better AIs.”
If AGI arrives in 2027, Dr. Yampolskiy’s estimate for superintelligence? “Say a year, two years after that that we hit ASI [Artificial Superintelligence]. That’s my prediction.”
So we’re potentially looking at:
- 2027: AGI (human-level intelligence across all domains)
- 2028-2029: Superintelligence (far beyond human intelligence)
Even if these dates slip by a few years, the fundamental challenge remains the same: we’re approaching these milestones faster than we’re developing safety mechanisms to handle them.
What Leading AI Companies Are Saying
Dr. Yampolskiy’s timeline isn’t an outlier. Major AI companies have made similar predictions:
- OpenAI: Internal forecasts suggest human-level AI within years, not decades
- Anthropic: Has stated expectations for “powerful AI systems” emerging in late 2026 or early 2027
- Google DeepMind: Co-founder Shane Legg estimated 50% probability of AGI by 2028
When the people actually building these systems believe they’re 2-5 years away from AGI, it’s time to take these timelines seriously.
The Current Signs We’re Almost There
According to Dr. Yampolskiy, we’re already seeing indicators that AGI is close:
1. Emergent Capabilities
AI systems are displaying abilities they weren’t explicitly trained for—a hallmark of general intelligence. “It’s creative. So it’s just like with a human being,” Dr. Yampolskiy notes.
2. Multi-Domain Expertise
Modern AI performs at superhuman levels across hundreds of different domains simultaneously—from coding to creative writing to complex reasoning. This breadth wasn’t possible even a year ago.
3. Learning to Learn
Self-play and self-training approaches have already produced superhuman performance in complex domains like the game of Go. “A system would play many many many games against itself. The better solutions, better agents would propagate those and after a while without any human data they became superhuman in those domains.”
Extending this to general problem-solving is the next logical step.
4. Real-World Impact
Perhaps most tellingly, top researchers are increasingly relying on AI for their actual work: “Now top scholars are relying more and more on it in their research.”
When the world’s leading scientists can’t do their best work without AI assistance, we’ve crossed a significant threshold.
The Narrow Window for Action
The urgency in Dr. Yampolskiy’s message stems from a simple calculation: if AGI is 2-3 years away and we don’t yet know how to control it safely, we’re running out of time to solve that problem.
“Once we create true super intelligence, a system more capable than any person in every domain, it’s very unlikely we’ll figure out how to indefinitely control it,” he warns.
The challenge isn’t just creating AGI—it’s creating AGI we can safely coexist with. And on that front, progress has been minimal.
Why This Should Matter to Everyone
Even if you’re not an AI researcher, the 2027 timeline has profound implications:
Economic Disruption Is Imminent
“Take self-driving cars. I think we are very close to having full self-driving without supervision,” Dr. Yampolskiy states. “The moment that happens, you have no reason to hire a commercial driver, right? All the truck drivers, all the Ubers, all of that gets automated as quickly as they can produce those systems.”
That’s potentially 6 million jobs in the U.S. alone—and that’s just one industry.
The Meaning Crisis
As Tom Bilyeu points out, when AI becomes better than humans at everything, we face an existential crisis of purpose. “When AI becomes better than you at everything, you run into a huge problem of now I have to like just sort of tell myself a story,” he notes.
Dr. Yampolskiy acknowledges this isn’t just about survival—it’s about “I-risk, ikigai risks, where we lost our meaning. The systems can be more creative. They can do all the jobs. It’s not obvious what you have to contribute to a world where superintelligence exists.”
The Control Question
Most importantly, once superintelligence arrives, humans may no longer be the ones making decisions about our collective future.
“If we’re still around, it’s because it decided for whatever game theoretic reasons to keep us around,” Dr. Yampolskiy states bluntly. “We’re definitely not in control and at any point it decides to take us out, it would be able to do so.”
The Counterargument: Could It Take Longer?
Not everyone agrees with aggressive timelines. Some researchers, like Meta’s Yann LeCun, argue that current approaches (large language models) will hit fundamental limitations.
Dr. Yampolskiy addresses this directly: “I think he’s not correct on this one.” His reasoning? “To predict the next term you need to create a model of the whole world because the token depends on everything about the world.”
In other words, to be good at predicting text, an AI must develop genuine understanding of reality—which is precisely what we mean by intelligence.
He adds: “I think JAN is known for making certain predictions about what models are capable of. And then within a week, people demonstrate that no, in fact, they can actually do that.”
What Happens If the Timeline Is Wrong?
Dr. Yampolskiy is characteristically direct: “Of course if it’s actually 5 to 10 years or anything slightly bigger it doesn’t matter. The problems are still the same.”
Whether AGI arrives in 2027, 2030, or 2035, the fundamental challenges remain:
- We don’t know how to control it
- Once created, it may be impossible to contain
- The competitive pressure ensures someone will build it
The specific year matters less than the fact that it’s coming soon and we’re unprepared.
What You Can Do
Dr. Yampolskiy’s message for different audiences:
For AI Developers
“If you are developing super intelligence, please stop. You’re not going to benefit yourself or others… Prove that you know how to control super intelligent systems no matter how capable they get, how much it scales.”
For Everyone Else
- Stay informed about AI developments
- Support AI safety research
- Prepare for rapid economic and social changes
- Develop adaptable skills that won’t be easily automated
Most importantly: Don’t dismiss this as science fiction. When prediction markets, leading AI companies, and pioneering researchers all converge on similar timelines, it’s time to take the possibility seriously.
The Bottom Line
AGI in 2027 isn’t certain—it’s a probability distribution, with significant weight on timelines ranging from 2-10 years. But the center of that distribution has shifted dramatically toward the near term.
As Dr. Yampolskiy concludes: “The best we can achieve is to buy us some time.”
The clock is ticking. The question isn’t whether AGI will arrive—it’s whether we’ll be ready when it does.
Learn more from Dr. Roman Yampolskiy’s full conversation on Tom Bilyeu’s Impact Theory podcast: “AI Scientist Warns Tom: Superintelligence Will Kill Us… SOON.” Dr. Yampolskiy is a tenured Associate Professor at the University of Louisville, founding director of the Cyber Security Lab, and author of “AI: Unexplainable, Unpredictable, Uncontrollable.”