I think I disagree. It's more informative to answer in terms of value as it would be measured today, not value after the economy adjusts.
Suppose someone from 1800 wants to figure out how big a deal mechanized farm equipment will be for humanity. They call up 2025 and ask "How big a portion of your economy is devoted to mechanized farm equipment, or farming enabled by mechanized equipment?" We give them a tiny number. They also ask about top-hats, and we also give them a tiny number. From these tiny numbers they conclude both mechanized farm equipment and top-hats won't be important for humanity.
EDIT The sort of situation I'm worried about your definition missing is if remote-worker AGI becomes too cheap to meter, but human hands are still valuable.
Would you agree your take is rather contrarian?
* This is not a parliamentary system. The President doesn't get booted from office when they lose majority support -- they have to be impeached[1].
* Successful impeachment takes 67 Senate votes.
* 25 states (half of Senate seats) voted for Trump 3 elections in a row (2016, 2020, 2024).
* So to impeach Trump, you'd need the votes of Senators from at least 9 states where Trump won 3 elections in a row.
* Betting markets expect (70% chance) Republicans to keep their 50 seats majority in the November Election, not a crash in support.
- ^
Or removed by the 25th amendment, which is strictly harder if the president protests (requires 2/3 vote to remove in both House and Senate).
...your modal estimate for the timing of Vance ascending to the presidency is more than two years before Trump's term ends?
$750 per books seems surprisingly reasonable to me as a royalty rate for a compulsory AI ingest license. Compulsory licenses are common in e.g. the music industry, you must license your musical work for covers (and get a 12¢ royalty per distribution)
I second the video recommendation.
A friend in China, in a rare conversation we had about international politics, was annoyed at US politicians for saying China was "supporting" Russia. "China has the production capacity to make easily 500,000 drones per day.[1]", he said. "If China were supporting Russia, the war would be over". And I had to admit I had not credited the Chinese government for keeping its insanely competitive companies from smuggling more drones into Russia.
- ^
This seemed like a drastic underestimate to me.
3Huh, I didn't expect to take Gary Marcus's side against yours but I do for almost all of these. If we take your two strongest cases:
- No massive advance (no GPT-5, or disappointing GPT-5)
- There was no GPT-5 in 2024? And there is still no GPT 5? People were talking in late 2023 like GPT 5 might come out in a few months, and they were wrong. The magic of "everything just gets better with scale" really seemed to slow after GPT-4?
- On reasoning models: I thought of reasoning models happening internally at Anthropic in 2023 and being distilled into public models, which was why Claude was so good at programming. But I could be wrong or have my timelines messed up.
- Modest lasting corporate adoption
- I'd say this is true? Read e.g. Dwarkesh talking about how he's pretty AI forward but even he has a lot of trouble getting AIs to do something useful. Many corporations are trying to get AIs to be useful in California, fewer elsewhere, and I'm not convinced these will last.
I don't think I really want to argue about these, more I find it weird people can in good faith have such different takes. I remember 2024 as a year I got continuously more bearish on LLM progress[1].
- ^
Until DeepSeek in late December.
1Cool, yes, I agree. But the reason other insiders don't like that public criticism is because it reduces their status by association. Your colleagues paid to get a position in a status hierarchy which you are devaluing, and they make you internalize those costs.
I don't think I agree at all. Relevant quotation, Larry Summers talking to Elizabeth Warren
> “Larry leaned back in his chair and offered me some advice. I had a choice. I could be an insider or I could be an outsider. Outsiders can say whatever they want. But people on the inside don’t listen to them. Insiders, however, get lots of access and a chance to push their ideas. People — powerful people — listen to what they have to say. But insiders also understand one unbreakable rule: They don’t criticize other insiders.
People come with features vectors of which clusters they are bucketed into (Harvard graduate, east bay rationalist, FTX employee, etc). Your reputation is tied to the reputation of that cluster, whether you want it to or not.
Whistleblowers are rare and their effects are minor. In-group cooperation and collusion is a large part of human affairs.
EDIT I agree with you, and just didn't understand what you said. My rephrase would be the article had it backwards:
Prediction: If consortment was less endorsement—if it were commonplace to spend time with your enemies—then it would be more commonplace to publicly report small wrongs.
This is reversed. It's the wrong-doers who are avoiding interactions with anyone who might publicly report small wrongs.
Back in the GOFAI days, when AI meant A* search, I remember thinking:
Now transformers appear to be good at System 1 reasoning, but computers aren't better at humans at everything. Why?
I think it comes down to:
Computers' System 1 is still wildly sub-human at sample efficiency; they're just billions of times faster than humans
LLM's work because they can train on an inhuman amount of reading material. When trained on only human amounts of material, they suck.
LLM Agents aren't very good because they can't learn on the job. Even dumb humans learn better instincts after a little on-the-job practice. We can just barely improve LLM's System 1 from its System 2, but only by brute forcing an inhuman number of roll-outs.
Robots suck, because the real world is slow and we don't have good tricks to train their System 1 by brute force.
We're in a weird paradigm where computers are billions of times faster than humans, but thousands of times worse at learning from a datum.