Thread

1. NichoPaolucci · 1789608442 · focus · HN ↗

   > OpenAI said it did not believe the industry “has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer.”

   Baffling. To my knowledge, they didn't properly airgap their systems. Keeping the genie in the box seems like 101 to me, and to "miss" that seems awfully fishy. This, among all of the Anthropic news, is an odd convergence.

   Maybe they're being truthful and it really is the end times.

   Maybe they've hit a wall in improvements, but I don't know enough on the topic to speak to that.

   Which is more likely?

   Either way, trying to sift through this can of worms is tiresome. I'm hopeful that this all comes to a head soon, what an exhausting few years it's been...

   1. abixb · 1789609922 · focus · HN ↗

      I'd wager on the second scenario. Anyone who's been paying attention to the industry knows that most of the 'gains' have come from test-time compute and architecting harnesses in novel ways. In my estimation, capability increases from "pre-training" alone died early last year, and we're now probably seeing test-time and other benchmark hacks approaching their limit as well.

      If you zoomed back to late-2024, people in the industry were predicting how we'd have AGI by now and the economy would've already 'taken off' with massive productivity growth and ushering in of great prosperity ('deflationary spiral'). Where is it? Where is the productivity growth? Where is the deflationary spiral?

      To be fair, models have gotten better in jagged ways, but reliability is far from usable, especially in long duration tasks, and there has been no effort by the AI companies to address the human brain's bandwidth bottleneck -- they hit the gas like there's no tomorrow and we have enormously capable but jaggedly intelligent multi-modal models with agentic capabilities that are only as effective as the human using it. This whole thing has become a giant mess.

      1. pu_pe · 1789631112 · focus · HN ↗

         How do you explain the fact that Qwen3.8 27B performs vastly better than any open model from even one year ago, if using the same test-time compute and harness?

         1. abixb · 1789655545 · focus · HN ↗

            "Vastly better" in what ways? Benchmarks? You know Benchmarks can be optimized for and benchmaxxed for, right?

            1. pu_pe · 1789668426 · focus · HN ↗

               It's obviously more capable in any task I tried (coding, translation, summarizing, etc). Benchmarks are not the only way to tell if a model is better or not.

               1. butlike · 1789760923 · focus · HN ↗

                  You gotta define 'obviously' here.

Unofficial Hacker News client; not affiliated with Y Combinator.