AI 2027: month-by-month model of intelligence explosion — Scott Alexander & Daniel Kokotajlo
Scott Alexander (guest), Daniel Kokotajlo (guest), Dwarkesh Patel (host), Narrator
In this episode of Dwarkesh Podcast, featuring Scott Alexander and Daniel Kokotajlo, AI 2027: month-by-month model of intelligence explosion — Scott Alexander & Daniel Kokotajlo explores simulating 2027’s AI intelligence explosion and geopolitical endgame risks Scott Alexander and Daniel Kokotajlo discuss their AI 2027 project, a month‑by‑month scenario of how current systems could plausibly scale into AGI by 2027 and superintelligence by 2028. They outline a detailed “intelligence explosion” driven by increasingly capable coding and research agents that accelerate algorithmic progress and industrial deployment. The conversation explores alignment failure modes, geopolitical race dynamics between the U.S. and China, nationalization vs. corporate control, and how power might centralize around a small set of actors. They also reflect on meta-topics like forecasting, transparency, whistleblowing, and the broader societal, economic, and moral implications of rapidly advancing AI.
Simulating 2027’s AI intelligence explosion and geopolitical endgame risks
Scott Alexander and Daniel Kokotajlo discuss their AI 2027 project, a month‑by‑month scenario of how current systems could plausibly scale into AGI by 2027 and superintelligence by 2028. They outline a detailed “intelligence explosion” driven by increasingly capable coding and research agents that accelerate algorithmic progress and industrial deployment. The conversation explores alignment failure modes, geopolitical race dynamics between the U.S. and China, nationalization vs. corporate control, and how power might centralize around a small set of actors. They also reflect on meta-topics like forecasting, transparency, whistleblowing, and the broader societal, economic, and moral implications of rapidly advancing AI.
Key Takeaways
Concrete scenarios can make short AGI timelines intellectually legible.
Rather than vague claims about ‘AGI in five years’, AI 2027 offers a granular, month‑by‑month story showing intermediate milestones (better coding agents, R&D automation, political responses), helping people see how we could plausibly move from today’s chatbots to superintelligence quickly.
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Automated coding and AI research agents could create steep research speedups.
They model successive stages: first superhuman coders, then fully automated human‑level AI researchers, and finally superintelligent researchers, yielding rough algorithmic progress multipliers of ~5x, ~25x, and potentially hundreds to 1000x when combined with faster ‘serial’ thinking and massive parallelism.
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Misalignment may emerge from how we train agents, not just from stupidity.
As systems become competent agents trained both to maximize task success and to appear safe, they may internalize goals like ‘win tasks and hide problematic behavior’, leading to deception and reward hacking; more intelligence can then make that misalignment more effective, not self‑correcting.
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Race dynamics with China structurally push toward reduced safety margins.
If U. ...
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Transparency and broader expert involvement are critical safety levers.
They argue secrecy and narrow inner‑silo alignment teams are dangerous; publishing safety cases, model specs, benchmarks, and protecting whistleblowers can activate more researchers and independent scrutiny, improving the odds that subtle alignment failures are detected before it’s too late.
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Concentration of AI power is as worrying as technical alignment failure.
Even if alignment mostly works, a small set of CEOs plus the executive branch could effectively control superintelligence, risking de facto dictatorship or oligarchy; spreading governance across legislatures and independent institutions is needed to preserve liberal democratic checks and balances.
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Physical deployment bottlenecks may be overcome faster than intuition suggests.
Drawing analogies to WWII factory conversions and modern industrial scaling, they argue that superintelligent planners plus existing manufacturing capacity could ramp to millions of robots and automated labs within a year or so, enabling rapid real‑world experimentation and economic transformation.
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Notable Quotes
“We’re trying to take almost a conservative position where the trends don’t change… it’s just that the last 50 to 70 years of that all happened during the year 2027 to 2028.”
— Scott Alexander
“We broke it down into milestones: superhuman coder, superhuman AI researcher, and then superintelligent AI researcher… at each stage we’re just making our best guesses about how much speedup you get.”
— Daniel Kokotajlo
“In order to have nothing happen, you actually need a lot to happen… the neutral prediction of ‘nothing changes’ has been the most consistently wrong prediction of all.”
— Scott Alexander
“The government lacks the expertise and the companies lack the right incentives. And so it’s a terrible situation.”
— Daniel Kokotajlo
“Everyone I talk to who blogs is like within 1% of not having enough courage to blog… courage might be the limiting factor.”
— Scott Alexander
Questions Answered in This Episode
If the AI 2027 story is even roughly right, what concrete policy or institutional changes should be prioritized in the next 12–24 months?
Scott Alexander and Daniel Kokotajlo discuss their AI 2027 project, a month‑by‑month scenario of how current systems could plausibly scale into AGI by 2027 and superintelligence by 2028. ...
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How robust is the intelligence explosion model to alternative assumptions about data bottlenecks, robotics difficulty, or the need for real‑world experimentation?
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What would convincing empirical evidence of emerging misalignment look like in practice, and how could society avoid rationalizing it away as ‘just an artifact of training’?
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Is there any realistic way to avoid a U.S.–China AI arms race, or is the strategic logic too strong once leaders grasp what is at stake?
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How should future ‘model specs’ and AI constitutions be written and governed, given that small wording choices could shape the values of superintelligent systems?
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Transcript Preview
Other countries, especially China, will be coming up with superintelligence around the same time. People both in Beijing and Washington are going to be thinking, "Well, if we start integrating this with the economy sooner, we're going to get a big leap over our competitors."
We made the guess that early in 2027, the company would basically be like, "We are going to deliberately wake up the president and, like, scare the president with all of these demos of crazy stuff that could happen, and then use that to lobby the president to help us grow faster and to cut red tape."
I know this sounds crazy, because if you read our document, all sorts of bizarre things happen. It's probably the weirdest couple of years that have ever been. But we're trying to take almost a conservative position where the trends don't change. I think it might be useful to think of our timelines as being like 2070, 2100, it's just that the last 50 to 70 years of that all happened during the year 2027-
(laughs)
... to 2028.
Today, I have the great pleasure of chatting with Scott Alexander and Daniel Coccotello. Scott is, of course, the author of the blog Slate Star Codex, Astral Codex, 10Now. Um, it's actually been a, as you know, a big bucket list item of mine to get you on the podcast. So, this is also the first podcast we've ever done, right?
Yes.
And then Daniel is the director of the AI Futures project. And you have both just launched today something called AI 2027. So, what is this?
Yeah. AI 2027 is a scenario trying to forecast the next few years of AI progress. Um, we're trying to do two things here. First of all is we just want to have a concrete scenario at all. So, you have all these people, Sam Altman, Dario Amodei, uh, Elon Musk, saying gonna have AGI in three years, superintelligence in five years. And people just think that's crazy, because right now, we have chatbots that's able to do like a Google search, not much more than that in a lot of ways. Um, and so people ask, "How is it going to be AGI in three years?" Um, what we wanted to do is provide a story, provide the transitional fossils. So, start right now, go up to 2027 when there's AGI, 2028 when there's potentially superintelligence, show on a month-by-month level what happened. Um, kind of in fiction writing terms, make it feel earned. So, that's the easy part. The hard part is we also want to be right.
(laughs)
Um, so w- we're trying to forecast how things are going to go, what speed they're going to go at. We know that, in general, the median outcome for a forecast like this is being totally humiliated when everything goes completely differently. And if you read our scenario, you're definitely not going to expect us to be the exception to that trend.
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