Which forecasters are you talking about? By your logic, the entire AI-2027 scenario is closer to an analysis because the authors shared most of their reasoning.
They make essentially 2 predictions on the first page and the rest of the 20 pages, is explaining their reasoning.
Compare this to AI 2027, just what they say for Early 2026: “Several competing publicly released AIs now match or exceed Agent-0, including an open-weights model. OpenBrain responds by releasing Agent-1, which is more capable and reliable.”—and what appears when you hover over what might appear to be a citation: “In practice, we expect OpenBrain to release models on a faster cadence than 8 months, but we refrain from describing all incremental releases for brevity.”
Why do they expect this? What are their sources? In what ways could they be biased? How do we know if their sources become outdated, or not as reliable?
We aren’t given this information.
Compare this to the prediction in Goldman Sachs: “The boost to global labor productivity could also be economically significant, and we estimate that AI could eventually increase annual global GDP by 7%.”
We’re given a specific source and specific data.
And what’s under their number? Is it another claim whose source we don’t know? No, it’s a specific source that we can look up and use to get more knowledge—and potentially use to make counterarguments to the prediction and analysis by Goldman Sachs, by either calling into question the value of the source, the interpretation of the data, or a number of other things.
Comparatively, how can we actually make counterarguments against AI 2027? How can we tell if their sources are high quality, or have been interpreted in ways that we would agree with?
I think AI 2027 makes much much more of an Argument From Authority, which I consider to be a reduction in the quality of discourse.
Regarding your question on AI 2027, I suspect that this is an extrapolation of pre-existing trends where novel models were released regularly: between Claude 3 Opus and o3 nearly every release shifted the leadership on the METR benchmark; as for open-weight models, we just had to see DeepSeek R1 outperform Claude 3.5 Sonnet (new) and extrapolate to an open-weight lab releasing DeepCent-1 outperforming the 8-month-old version of Agent-0.
Which forecasters are you talking about? By your logic, the entire AI-2027 scenario is closer to an analysis because the authors shared most of their reasoning.
Here is a Goldman Sachs report: https://www.ansa.it/documents/1680080409454_ert.pdf
They make essentially 2 predictions on the first page and the rest of the 20 pages, is explaining their reasoning.
Compare this to AI 2027, just what they say for Early 2026: “Several competing publicly released AIs now match or exceed Agent-0, including an open-weights model. OpenBrain responds by releasing Agent-1, which is more capable and reliable.”—and what appears when you hover over what might appear to be a citation: “In practice, we expect OpenBrain to release models on a faster cadence than 8 months, but we refrain from describing all incremental releases for brevity.”
Why do they expect this? What are their sources? In what ways could they be biased? How do we know if their sources become outdated, or not as reliable?
We aren’t given this information.
Compare this to the prediction in Goldman Sachs: “The boost to global labor productivity could also be economically significant, and we estimate that AI could eventually increase annual global GDP by 7%.”
We’re given a specific source and specific data.
And what’s under their number? Is it another claim whose source we don’t know? No, it’s a specific source that we can look up and use to get more knowledge—and potentially use to make counterarguments to the prediction and analysis by Goldman Sachs, by either calling into question the value of the source, the interpretation of the data, or a number of other things.
Comparatively, how can we actually make counterarguments against AI 2027? How can we tell if their sources are high quality, or have been interpreted in ways that we would agree with?
I think AI 2027 makes much much more of an Argument From Authority, which I consider to be a reduction in the quality of discourse.
I think that the compute forecast of AI-2027 had the AI-2027 authors almost fully explain their reasoning, as did the security forecast where the AI hacking capabilities are estimated by existing benchmarks like Cybench, the timelines forecast and the takeoff forecast where the authors tried to elicit as much information as they could. [1]
Regarding your question on AI 2027, I suspect that this is an extrapolation of pre-existing trends where novel models were released regularly: between Claude 3 Opus and o3 nearly every release shifted the leadership on the METR benchmark; as for open-weight models, we just had to see DeepSeek R1 outperform Claude 3.5 Sonnet (new) and extrapolate to an open-weight lab releasing DeepCent-1 outperforming the 8-month-old version of Agent-0.
There also is the AI goals forecast which is closer to a sum of conjectures.