"We believe our internal AI R&D efforts are significantly faster than they would be without AI assistance, but not yet by a factor of 2 (though we are uncertain and measurement is difficult)"

So Anthropic thinks their productivity is not even doubled by AI. Interesting data point.

It is also difficult to measure this in the face of ever shifting baselines. Anthropic is built on AI from the ground up. I'm not picking a side, but Anthropic getting a 2x increase is different bar than some random enterprise shipping legacy apps with clunky processes getting a 2x increase by adopting.

R&D productivity. Pretty sure they claim more for e.g. Claude Code.

Yeah, marketing numbers and internal ops numbers rarely line up. We've seen same thing with our own tools, demo doubles output but actual sprint velocity barely moves once you count review time and cleanup.

>thinks

They can’t measure even measure it, it’s just vibes. They may not even be more productive.

Ran similar internal timing tests on a smaller team last year, comparing PR cycle time before/after Claude assistance. Signal was there but noisy as hell, maybe 20-30% on routine tasks, way less on anything architecturally hairy. Self-reported multiplier estimates are almost always inflated.

So they did two years of work in one year? yeah right lol

So I can take that as ~infinite amount of tokens don't don't even get you 2x on any novel tasks? No auto-researcher, no rsi..

Is that correct?

So as of a month ago their best internal model was "somewhat more capable" than Mythos "but does not display a capability jump of the degree observed from Claude Opus 4.6 to Mythos Preview." I thought they would have a significantly more capable model by then, more than five months after Mythos finished training. They'd better have one by now, or the Chinese competitors are closer to catching up than I thought.

You thought they were gonna double the model size again?

Also it occurs to me that they're somewhat incentivized to downplay cyber risks after what happened last time...

I'm still uncertain if mythos is real. Subsequent model releases have been lackluster, no one has claimed to verify mythos performance and it's silently vanished from most comparisons.

AI companies are flooding the zone like Steve Bannon. Leave no one time to develop thoughts.

If they leave Steve Bannon with no time to develop thoughts, I'll tip one out for them.

Reminds me less of Bannon's "flood the zone with shit" and more of standard attention economics: publish frequently enough and critique can't keep pace with output. Same dynamic shows up in any fast-moving research field, not unique to labs.

"all traffic through our systems for collecting human feedback data from contractors evaluating our models ran without blocking biological classifiers"

"totaled around 133M exchanges."

While this wound up being relatively benign, I still find this concerning, amidst numerous sandbox escapes, and previously, unreleased models being accessible via a custom URL. I don't think these companies are giving the responsibility they possess enough weight. How many more issues like this exist?

It's crazy how Anthropic talks so much about their "AGI risk" and not enough about the risk of bankruptcy.

Are you surprised? Why would any private company spend time publicizing their financial risks?

Seems like a strange expectation.

Does anybody have any good reading on how the Chinese labs approach risk vs. the American ones?

If US model hacks US government, that's Very Bad. (China did this last year with Claude Code.)

If Chinese model hacks US government... free marketing?

Concordia AI's tracker good starting point but if want raw primary docs, check each lab's own safety framework — Baidu, Alibaba, Zhipu all published versions. Diff em against Anthropic's RSP side by side, gaps jump out fast.

a mystery “model 2” is mentioned alongside mythos/fable.

> Model 2, which is somewhat more capable than Mythos 5. Our rough qualitative sense is that this model is a noticeable improvement on Mythos 5 for many tasks relevant to internal use but does not display a capability jump of the degree observed from Claude Opus 4.6 to Mythos Preview. We do not currently have plans to release this model externally, and have not run all of our typical suite of predeployment assessments, so we have somewhat lower confidence in our beliefs about its capabilities.

"Yeah, you wouldn't know her, she goes to another school."

Meanwhile I can't really tell the difference between Fable and Opus for my tasks. I kinda think Fable does a better UX work so I keep using it for that because I couldn't be bothered to A/B them, but otherwise it's all the same and the model and effort are just feel good knobs I twist to still remain a load-bearing element. At least that's my honest take.

Fable was amazing during the first preview. Once they added it back, the limits are too low to get anything done. I might use it in chat if I remember to select it once a month but don’t even bother to try and code with it.

For the past 2 weeks or so I've been doing the A/B test, sending identical prompts to Fable 5 and Opus 5 to test their ability to produce design documents for new feature work. I've consistently found that Opus 5 produces more complete, accurate and "imaginative" designs than Fable, often finding design issues or nearby bugs that Fable 5 misses. However, that creativity means Opus seems to hallucinate more, while Fable's design is clearly based on the actual existing code. Or as Opus put it: "I hedged — [Fable] checked."

By pitting them against each other I get much better design work, and then I've been happy to hand off the design file to Opus 5 for implementation. But some of the assumptions Opus 5 makes leaves me wary of relying on it too strongly. This might be fixable by prompting it to ground its answers.

> More capable than Mythos 5 in some areas, less capable in others; overall slightly more capable.

This sounds like it might be a Mythos finetune for some specific task.

EDIT: After reading some more reading, it looks like model 2 might be an AI research fine tune based off the section 3.4.3 CoBench

> 5.3 Benefits from Anthropic’s operating as a frontier AI company

It does feel they are trying to ask the government to lock the market for us.

> However, we are less confident in this assessment than we were in prior risk reports, since our most concrete task-based evaluations have “saturated”—i.e., no longer capture increases in models’ capabilities—and because we are seeing early signs of acceleration.

I totally understand this is a subset of alignment-related evals, but if Anthropic of all is running out of evals, doesn't that also means we are running out of things to scale?

I mean. I totally believe they have a model that is better at Kernel Optimization, creating new matrix multiplication algos, than Mythos. But it's clearly no generalizing, rightw

What am I missing?

My friends and I, and the teams I'm a part of, just want to build and create fun, cool things. I am so tired of being preached to by Anthropic like they're some arbiter of 'ethics.' So, so tired.

You’re allowed to switch to the competition, including open models

dgellow -> 1st Stainless engineer, 2022-26 (bought by Anthropic).

LOLOLOL

> 6.2 [Appendix redacted] > This appendix describes the criteria for our blocking bioclassifier exemption policy, and has been redacted from the public version of this report for security reasons.

>6.3 [Appendix redacted] > This appendix, redacted from the public version of this report, details the changes made to our constitution to expand classifier coverage to harmful uses in scope for the CB-2 threat model but not the CB-1 threat model, as described in Section 4.5.2.1.

interesting...

EDIT: After reading more I'd recommend looking at Transcript 2.20.A. Its a transcript of claude going over the redactions in the report. The section says its specifically for section 2, but the transcript also mentions other sections.

Doubt bankruptcy risk and AGI risk are separate things. Burn rate exists because they're racing capability, so the two arguments loop back into each other whether people like it or not.