In this post, I'll go through some of my best guesses for the current situation in AI as of the start of April 2026. You can think of this as a scenario forecast, but for the present (which is already uncertain!) rather than the future. I will generally state my best guess without argumentation and without explaining my level of confidence: some of these claims are highly speculative while others are better grounded, certainly some will be wrong. I tried to make it clear which claims are relatively speculative by saying something like "I guess", "I expect", etc. (but I may have missed some).

You can think of this post as more like a list of my current views rather than a structured post with a thesis, but I think it...

Note: you are ineligible to complete this challenge if you’ve studied Ancient or Modern Greek, or if you natively speak Modern Greek, or if for other reasons you know what mistakes I’m claiming Opus 4.6 makes. If you’re ineligible, please don’t help other people complete the challenge.

I have recently started using Claude Opus 4.6 to start studying Ancient Greek. Specifically, I initially used it to grade problem sets at the end of the textbook I’ve been using, but then I got worried about it being sycophantic towards my answers, so started having it just write out the answers itself.

I recently gave it this prompt, from the end of Chapter 3 of my textbook:

Can you write out the answers to this Ancient Greek fill-in-the-blanks exercise so

I've recently updated towards substantially shorter AI timelines and much faster progress in some areas. ^[1] The largest updates I've made are (1) an almost 2x higher probability of full AI R&D automation by EOY 2028 (I'm now a bit below 30% ^[2] while I was previously expecting around 15%; my guesses are pretty reflectively unstable) and (2) I expect much stronger short-term performance on massive and pretty difficult but easy-and-cheap-to-verify software engineering (SWE) tasks that don't require that much novel ideation ^[3] . For instance, I expect that by EOY 2026, AIs will have a 50%-reliability...

This post reflects my personal opinion and not necessarily that of other members of Apollo Research.

TLDR: I think funders should heavily incentivize AI safety work that enables spending $100M+ in compute or API budgets on automated AI labor that directly and differentially translates to safety.

Motivation

I think we are in a short timeline world (and we should take the possibility seriously even if we don't have full confidence yet). This means that I think funders should aim to allocate large amounts of money (e.g. $1-50B per year across the ecosystem) on AI safety in the next 2-3 years. 

I think that the AI safety funders have been allocating way too little funding and their spending has been far too conservative in the past 5 years. So, in my opinion,...

Written quickly as part of the Inkhaven Residency.

At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories:

• Doing quick sanity checks
• Saying precisely what you want to say
• Asking why one more time

In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. 

This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. 

Doing quick sanity checks

Research is hard (almost by definition) and people are often wrong. Every...

I recently wrote about complete feedback, an idea which I think is quite important for AI safety. However, my note was quite brief, explaining the idea only to my closest research-friends. This post aims to bridge one of the inferential gaps to that idea. I also expect that the perspective-shift described here has some value on its own.

In classical Bayesianism, prediction and evidence are two different sorts of things. A prediction is a probability (or, more generally, a probability distribution); evidence is an observation (or set of observations). These two things have different type signatures. They also fall on opposite sides of the agent-environment division: we think of predictions as supplied by agents, and evidence as supplied by environments.

In Radical Probabilism, this division is not so strict....