The era of making AI smarter just by making it bigger is ending. But that doesn’t mean progress is slowing down — far from it. AI models continue to get much more powerful, just using very different methods, and those underlying technical changes force a big rethink of what coming years will look like.

Toby Ord — Oxford philosopher and bestselling author of The Precipice — has been tracking these shifts and mapping out the implications both for governments and our lives.

Links to learn more, video, highlights, and full transcript: https://80k.info/to25

As he explains, until recently anyone can access the best AI in the world “for less than the price of a can of Coke.” But unfortunately, that’s over.

What changed? AI companies first made models smarter by throwing a million times as much computing power at them during training, to make them better at predicting the next word. But with high quality data drying up, that approach petered out in 2024.

So they pivoted to something radically different: instead of training smarter models, they’re giving existing models dramatically more time to think — leading to the rise in “reasoning models” that are at the frontier today.

The results are impressive but this extra computing time comes at a cost: OpenAI’s o3 reasoning model achieved stunning results on a famous AI test by writing an Encyclopedia Britannica’s worth of reasoning to solve individual problems at a cost of over $1,000 per question.

This isn’t just technical trivia: if this improvement method sticks, it will change much about how the AI revolution plays out, starting with the fact that we can expect the rich and powerful to get access to the best AI models well before the rest of us.

Toby and host Rob discuss the implications of all that, plus the return of reinforcement learning (and resulting increase in deception), and Toby's commitment to clarifying the misleading graphs coming out of AI companies — to separate the snake oil and fads from the reality of what's likely a "transformative moment in human history."

Recorded on May 23, 2025.

Chapters:

Cold open (00:00:00)
Toby Ord is back — for a 4th time! (00:01:20)
Everything has changed (and changed again) since 2020 (00:01:37)
Is x-risk up or down? (00:07:47)
The new scaling era: compute at inference (00:09:12)
Inference scaling means less concentration (00:31:21)
Will rich people get access to AGI first? Will the rest of us even know? (00:35:11)
The new regime makes 'compute governance' harder (00:41:08)
How 'IDA' might let AI blast past human level — or not (00:50:14)
Reinforcement learning brings back 'reward hacking' agents (01:04:56)
Will we get warning shots? Will they even help? (01:14:41)
The scaling paradox (01:22:09)
Misleading charts from AI companies (01:30:55)
Policy debates should dream much bigger (01:43:04)
Scientific moratoriums have worked before (01:56:04)
Might AI 'go rogue' early on? (02:13:16)
Lamps are regulated much more than AI (02:20:55)
Companies made a strategic error shooting down SB 1047 (02:29:57)
Companies should build in emergency brakes for their AI (02:35:49)
Toby's bottom lines (02:44:32)

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Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Transcriptions and web: Katy Moore

#219 – Toby Ord on graphs AI companies would prefer you didn't (fully) understand

80,000 Hours Podcast

#219 – Toby Ord on graphs AI companies would prefer you didn't (fully) understand