Regarding explicit model parameters, the most important differences for AC timelines are:
1. Daniel's median present doubling time being 4 months rather than 4.5 months. My sense is that this is Daniel giving a bit less weight than me to a reversion to something in between the current trend and older, slower trend.
My sense is that this disagreement is mostly driven by (a) Daniel thinking that lower reliability/success rate is required for AC than I do (b) Daniel thinking that the relevant tasks that AC needs to automate take less time than I do, in part because of differences around how to count time for tasks that are done by many humans in parallel. Also Daniel has some reasoning about the AC only needing to automate long enough tasks to reach escape velocity where it's getting better fast enough that the tasks it needs to do are always shorter than its time horizon, which I don't feel like I fully understand.
Regarding other AC timelines considerations that creep into our all-things-considered view, my guess is that the biggest difference is a pure vibes-level difference where it feels intuitively to Daniel like extrapolating progress in coding agents leads to AC in 2027 or maybe 2028, while my vibes say it will take longer.
Regarding takeoff post-AC (especially post-SAR), the biggest driver of differences is that Daniel has a median automated research taste slope (on the homepage, "How quickly AIs improve at research taste") of 3.0 while I have 2.1. As you can see in https://www.aifuturesmodel.com/analysis this is by far the most important parameter for takeoff. And if I only adjust that parameter of Daniel's to match mine, AC->ASI takeoff goes from 1 to 1.75 years. I have a long writeup/spreadsheet which explains where I got 2.1, and Daniel wrote a short blurb about why he sets it higher: https://docs.google.com/document/d/1ru6Okbxb6XuH18Cz8439sdQJazMV39hNxsWDokh97r0/edit?tab=t.0#heading=h.y0yy6iou4a4q
> The AC milestone is the point at which an AGI company would rather lay off all of their human software engineers than stop using AIs for software engineering.
This operationalization seems unideally ambiguous to me, such that I'd be surprised if this was the best operationalization of "AC" to focus your forecasting efforts on.[1]
I'm sure you've considered this, but wanted to flag this anyways.
[1] It's a purely hypothetical decision that no company actually faces, and if there's a dispute about whether AC has been reached or not (even between Daniel and Eli), you can't even say "well let's imagine what would happen if the AGI company made each choice" as a way to resolve the dispute, since presumably the company's development would suffer significantly and it would fall behind and go out of business regardless of which choice it makes--unless you're *way past* the AC milestone, in which case sure, laying off all the human SEs is fine, but the point is for the milestone to be able to measure the threshold, not the point where you're so far past it that obviously you'd rather lay off all the human SEs. Wait, or is this what you meant by the milestone? The point where the company would *obviously* rather lay off the human SEs because they aren't really needed anymore? If so, I still think it's a little too ambiguous. There's a spectrum from "laying off all our human SEs would be catastrophically harmful to us as a company" to "it'd be a big setback, but we could manage it" to "we fired them already because paying them to do software engineering wasn't worth the money". And unless companies go through that whole spectrum in a short period of time, then what point is meant by your AC milestone is still unclear to me.
Did you consider "when AI_spend reaches a certain fraction of the company payroll" as a milestone? Layoffs are a terrible metric because of all the labor laws, etc., but when you see AI token spend approaching SE salary spend you know the company values "AI coders" as much as human ones.
Perhaps low priority and you guys have more important things to do, but it sure would be swell if you could update https://forecast2026.ai/ with the Q1 updated current values so we could see how our predictions were faring so far.
In predicting the arrival of AGI, you operationalize AGI as “AIs at least as good as top human experts at virtually all cognitive tasks.” Without operationalizing “virtually,” this definition is useless.
It’s like predicting when spaceships can go “almost” the speed of light. Energy required approaches infinity; 99% is much harder than 98%.
Terrifying and substantial, but not total (as you guys are aware). Excuse me while I obsess over this for the next week, observing everything I can and discussing with others.
Just wanted to say that the work you do is important and appreciated.
What accounts for the difference between Eli and Daniel? Are there a few key disagreements (and how large are those disagreements)?
Regarding explicit model parameters, the most important differences for AC timelines are:
1. Daniel's median present doubling time being 4 months rather than 4.5 months. My sense is that this is Daniel giving a bit less weight than me to a reversion to something in between the current trend and older, slower trend.
If we make this change, it moves AC with Daniel's median parameters back from 06/2028 to 10/2028 (see https://www.aifuturesmodel.com/p?base=daniel-04-02-26&pdt=0.3748905706229262)
2. Daniel has a median estimate of 1 year for the 80% time horizon required for AC, while I have 125.
Adjusting this moves AC from 10/2028 to 12/2029: https://www.aifuturesmodel.com/p?base=daniel-04-02-26&pdt=0.3748905706229262&acth=7.197271450951031. Which is nearly exactly the same as my AC with median parameters of 11/2029 (https://www.aifuturesmodel.com/p?base=eli-04-02-26)
My sense is that this disagreement is mostly driven by (a) Daniel thinking that lower reliability/success rate is required for AC than I do (b) Daniel thinking that the relevant tasks that AC needs to automate take less time than I do, in part because of differences around how to count time for tasks that are done by many humans in parallel. Also Daniel has some reasoning about the AC only needing to automate long enough tasks to reach escape velocity where it's getting better fast enough that the tasks it needs to do are always shorter than its time horizon, which I don't feel like I fully understand.
You can see each of our rationales at https://docs.google.com/document/d/1ru6Okbxb6XuH18Cz8439sdQJazMV39hNxsWDokh97r0/edit?tab=t.0#heading=h.ga01t71wyiv7
Regarding other AC timelines considerations that creep into our all-things-considered view, my guess is that the biggest difference is a pure vibes-level difference where it feels intuitively to Daniel like extrapolating progress in coding agents leads to AC in 2027 or maybe 2028, while my vibes say it will take longer.
Regarding takeoff post-AC (especially post-SAR), the biggest driver of differences is that Daniel has a median automated research taste slope (on the homepage, "How quickly AIs improve at research taste") of 3.0 while I have 2.1. As you can see in https://www.aifuturesmodel.com/analysis this is by far the most important parameter for takeoff. And if I only adjust that parameter of Daniel's to match mine, AC->ASI takeoff goes from 1 to 1.75 years. I have a long writeup/spreadsheet which explains where I got 2.1, and Daniel wrote a short blurb about why he sets it higher: https://docs.google.com/document/d/1ru6Okbxb6XuH18Cz8439sdQJazMV39hNxsWDokh97r0/edit?tab=t.0#heading=h.y0yy6iou4a4q
Love the intellectual honesty 🙏.
> The AC milestone is the point at which an AGI company would rather lay off all of their human software engineers than stop using AIs for software engineering.
This operationalization seems unideally ambiguous to me, such that I'd be surprised if this was the best operationalization of "AC" to focus your forecasting efforts on.[1]
I'm sure you've considered this, but wanted to flag this anyways.
[1] It's a purely hypothetical decision that no company actually faces, and if there's a dispute about whether AC has been reached or not (even between Daniel and Eli), you can't even say "well let's imagine what would happen if the AGI company made each choice" as a way to resolve the dispute, since presumably the company's development would suffer significantly and it would fall behind and go out of business regardless of which choice it makes--unless you're *way past* the AC milestone, in which case sure, laying off all the human SEs is fine, but the point is for the milestone to be able to measure the threshold, not the point where you're so far past it that obviously you'd rather lay off all the human SEs. Wait, or is this what you meant by the milestone? The point where the company would *obviously* rather lay off the human SEs because they aren't really needed anymore? If so, I still think it's a little too ambiguous. There's a spectrum from "laying off all our human SEs would be catastrophically harmful to us as a company" to "it'd be a big setback, but we could manage it" to "we fired them already because paying them to do software engineering wasn't worth the money". And unless companies go through that whole spectrum in a short period of time, then what point is meant by your AC milestone is still unclear to me.
I acknowledge those weaknesses in the milestone; got a better idea? We considered various alternatives which all seemed worse.
Did you consider "when AI_spend reaches a certain fraction of the company payroll" as a milestone? Layoffs are a terrible metric because of all the labor laws, etc., but when you see AI token spend approaching SE salary spend you know the company values "AI coders" as much as human ones.
Is there any planned publication date for the next planned scenario that ya’ll would be able to share?
We have a draft but it's unclear how long it'll take to be ready to publish, sorry!
All good. I’m grateful to be able to read you guys’ hard work for free. I wish ya’ll luck
Perhaps low priority and you guys have more important things to do, but it sure would be swell if you could update https://forecast2026.ai/ with the Q1 updated current values so we could see how our predictions were faring so far.
In predicting the arrival of AGI, you operationalize AGI as “AIs at least as good as top human experts at virtually all cognitive tasks.” Without operationalizing “virtually,” this definition is useless.
It’s like predicting when spaceships can go “almost” the speed of light. Energy required approaches infinity; 99% is much harder than 98%.
6 AI leaders' AGI timeline predictions vs your updated forecast:
Musk: 2026 (25% accuracy, FADE)
Altman: AI researcher by Mar 2028 (73%)
Amodei: virtual colleague 2026 (58%)
Jensen: 5 years = 2030 (61%)
Hassabis: 5-10 years
LeCun: skeptic (62%)
Your mid-2028 AC median aligns closest with Altman, who has the highest accuracy in this group.
Data: ClaimClock
Terrifying and substantial, but not total (as you guys are aware). Excuse me while I obsess over this for the next week, observing everything I can and discussing with others.