I think it's possible the amount of new software that will be written for an audience of 1-10 will be greater in 2026 than in any previous year, and then the same again for many years to come. I also think a lot of this software will be essentially 'hidden' - people just writing this stuff for themselves because the cost to say things to an agent is very low compared with the cost of actually planning out a software design and so forth.

Interoperability will probably be important in the next few years and I wonder if this is something solvable at the agent/LLM level (standing instructions like 'typically, use sqlite, use plaintext, use open standards' or whatever). I also think observability and ops will be pretty important - many people who want personal software but don't care for the maintenance and upkeep.

But in general, yeah, I agree, I think they would think it was a sentient, conscious, emotional being. And then the question is - why do we not think that now?

As I said, I don't have a particularly strong opinion, but it's very interesting (and fun!) to think about.

In general I was wondering about what I would have thought seeing Claude today side-by-side with the original ChatGPT, and then going back further to GPT-2 or BERT (which I used to generate stochastic 'poetry' back in 2019). And then… what about before? Markov chains? How far back do I need to go where it flips from thinking that it's "impressive but technically explainable emergent behaviour of a computer program" to "this is a sentient being". 1991 is probably too far, I'd say maybe pre-Matrix 1999 is a good point, but that depends on a lot of cultural priors and so on as well.

so I'm just telling you they did the thing you said you wanted.

I don't come down particularly hard on either side of the model sapience discussion, but I don't think dismissing either direction out of hand is the right call.

Well, I would say they have done precisely that in evaluating the model, no? For example section 2.2.5.1:

>Uplift and feasibility results

>The median expert assessed the model as a force-multiplier that saves meaningful time (uplift level 2 of 4), with only two biology experts rating it comparable to consulting a knowledgeable specialist (level 3). No expert assigned the highest rating. Most experts were able to iterate with the model toward a plan they judged as having only narrow gaps, but feasibility scores reflected that substantial outside expertise remained necessary to close them.

Other similar examples also in the system card

> 2.1.3.2 On chemical and biological risks

> We believe that Mythos Preview does not pass this threshold due to its noted limitations in open-ended scientific reasoning, strategic judgment, and hypothesis triage. As such, we consider the uplift of threat actors without the ability to develop such weapons to be limited (with uncertainty about the extent to which weapons development by threat actors with existing expertise may be accelerated), even if we were to release the model for general availability. The overall picture is similar to the one from our most recent Risk Report.

Interesting to see that they will not be releasing Mythos generally. [edit: Mythos Preview generally - fair to say they may release a similar model but not this exact one]

I'm still reading the system card but here's a little highlight:

> Early indications in the training of Claude Mythos Preview suggested that the model was likely to have very strong general capabilities. We were sufficiently concerned about the potential risks of such a model that, for the first time, we arranged a 24-hour period of internal alignment review (discussed in the alignment assessment) before deploying an early version of the model for widespread internal use. This was in order to gain assurance against the model causing damage when interacting with internal infrastructure.

and interestingly:

> To be explicit, the decision not to make this model generally available does _not_ stem from Responsible Scaling Policy requirements.

Also really worth reading is section 7.2 which describes how the model "feels" to interact with. That's also what I remember from their release of Opus 4.5 in November - in a video an Anthropic employee described how they 'trusted' Opus to do more with less supervision. I think that is a pretty valuable benchmark at a certain level of 'intelligence'. Few of my co-workers could pass SWEBench but I would trust quite a few of them, and it's not entirely the same set.

Also very interesting is that they believe Mythos is higher risk than past models as an autonomous saboteur, to the point they've published a separate risk report for that specific threat model: https://www-cdn.anthropic.com/79c2d46d997783b9d2fb3241de4321...

The threat model in question:

> An AI model with access to powerful affordances within an organization could use its affordances to autonomously exploit, manipulate, or tamper with that organization’s systems or decision-making in a way that raises the risk of future significantly harmful outcomes (e.g. by altering the results of AI safety research).